Lipid tumour profile

ABSTRACT

We describe a method of generating a classification model capable of distinguishing between two biological states, the method comprising the steps of: (a) providing a training dataset, X, comprising concentrations of a plurality of lipids in a biological sample in a first state and a biological sample in a second state; (b) subjecting the training dataset X to Principal Components Analysis (PCA), in which the PCA analysis generates a transformation matrix, C, and a transformed dataset, Y l ; (c) subjecting the transformed dataset Y l  to Support Vector Machines (SVM) analysis, in which the SVM analysis generates a SVM model, S; (d) forming a classification model comprising (i) the transformation matrix C, and (ii) the corresponding SVM model S.

FIELD

The present invention relates to the fields of medicine, cell biology, molecular biology and genetics.

BACKGROUND

Ovarian tumor is the fifth leading cause of cancer-related death and is difficult to diagnose and monitor. Although ovarian cancer accounts for about 4 percent of all cancers in women, it has the highest mortality of all gynecologic cancers, being a silent killer because it is often diagnosed at an advanced stage.

Ovarian cancer may be diagnosed, in part, by collecting a routine medical history from a patient and by performing physical examination, x-ray examination, and chemical and hematological studies on the patient. Hematological tests which may be indicative of ovarian cancer in a patient include analyses of serum levels of proteins designated CA125 and DF3 and plasma levels of lysophosphatidic acid (LPA). Palpation of the ovaries and ultrasound techniques (particularly including endovaginal ultrasound and color Doppler flow ultrasound techniques) can aid detection of ovarian tumors and differentiation of ovarian cancer from benign ovarian cysts. However, a definitive diagnosis of ovarian cancer typically requires performing exploratory laparotomy of the patient.

Thus, while abnormal growth corresponding to ovaries can be monitored by ultrasonography or by the serum marker, CA125, determining whether the growth is benign or malignant is not possible without biopsies. Biopsies are typically done after surgery and the tissue growth is taken for various pathological examinations. This is a tedious and time consuming procedure. The patient is usually out of the surgery room by the time the information is acquired.

Detection and proper measures for the control of the disease by surgery at the early stages can prevent the patient from undergoing agonizing radio and chemotherapy. Such procedures also often, in the most advanced cases, have no substantial curative effects.

Current diagnostic methods include serological diagnostics, which determine the level of CA-125, a protein produced by ovarian cancer cells. While CA-125 is an important test, it unfortunately is not always accurate. For example, some ovarian cancers may not produce enough CA-125 levels to cause a positive test result. Although the level of this antigen is elevated in nearly 80% of the patients with advanced stages of the cancer, expression may be low in early stages of ovarian cancer. Serum CA125 levels may also be falsely elevated in patients having other gynecological conditions like serosa of the peritoneum or pericardium, uterine fibroids, renal disorders or even pregnancy and normal menses.

Other current diagnostic methods include trans-vaginal ultrasound (TVU), which may be conducted alone or in combination with CA-125 testing. These methods can detect ovarian cancer but can also produce many false-positive test results. Use of ultrasound in conjunction with the CA125 has a positive predictive value of only 20%. It is estimated that three out of every four surgeries carried out are for non-malignant conditions, and these could possibly have been avoided saving the patients from the surgical trauma.

The diagnostic and prognostic tools presently available are therefore not adequate to predict the onset of the disease.

Genomic and proteomic techniques for the discovery of novel biomarkers for ovarian cancer from body fluids are known. The blueprints derived from these methods have recently shown assurance for early ovarian cancer detection, but further studies regarding their reproducibility and reliability for early detection and screening are needed.

Some of these proteins which are predicted to have a role as biomarker for ovarian cancer include M-CSF, mesothelin, α-folate receptor, OVX1, CA72-4, Prostasin, Osteopontin, Inhibin and Kallikrien.

Recently there has been an interest in using lysophosphatidic acid (LPA) as a biomarker for ovarian cancer. LPA has been shown to stimulate the proliferation of ovarian cancer cells and has been found in blood of ovarian cancer patients. LPA as a biomarker has been shown to have a sensitivity of 100% in advanced stage and almost 90% in the early stages.

However, the use of LPA as a biomarker for ovarian cancer has been highly controversial. In some studies no significant change in LPA levels in the ovarian cancer patients have been observed, raising questions about the utility of LPA as a biomarker. Some of the discrepancies were attributed to differences in the isolation protocol of plasma as activated platelets could also generate LPA. This lipid is also elevated in patients with other gynecological manifestations.

There is therefore a need for a method of detecting and diagnosing cancers such as ovarian cancers.

SUMMARY

According to a 1^(st) aspect of the present invention, we provide a method of generating a classification model capable of distinguishing between two biological states, the method comprising the steps of: (a) providing a training dataset, X, comprising concentrations of a plurality of lipids in a biological sample in a first state and a biological sample in a second state; (b) subjecting the training dataset X to Principal Components Analysis (PCA), in which the PCA analysis generates a transformation matrix, C, and a transformed dataset, Y_(l); (c) subjecting the transformed dataset Y_(l) to Support Vector Machines (SVM) analysis, in which the SVM analysis generates a SVM model, S; (d) forming a classification model comprising (i) the transformation matrix C, and (ii) the corresponding SVM model S.

The plurality of lipids may include a plurality of choline lipids, such as phosphatidylcholine (GPCho) or sphingomyelin (SM) or both. It may further optionally comprise one or more of phosphatidic acid (GPA), phosphatidylglycerol (GPGro), phosphatidylserine acid (GPSer), sulfatides, cardiolipin, phosphatidylethanolamine (GPEtn), phosphatidylinositol (GPIns), phosphatidylinositol phosphates (GPInsPs), ceramide (Cer), mono hexosyl ceramide (MonoHexCer) and di hexosyl ceramide (DiHexCer). It may comprise all the aforementioned lipids.

The plurality of lipids may comprise the lipids set out in Table D1. It may comprise the lipids set out in Table D2. It may comprise the lipids set out in Table D3. It may comprise the lipids set out in Table E4. It may comprise the lipids set out in Table E6.

The two biological states may comprise a normal state and a diseased state. The diseased state may comprise a cancerous or tumour state, for example ovarian cancer.

The classification model may be capable of achieving a sensitivity of 98.99% or more. It may be capable of achieving specificity of 92.31% or more. It may be capable of achieving a PPV of 97.03% or more. It may be capable of achieving a NPV of 97.30%. It may be capable of achieving more or an accuracy of 97.10% or more. It may be capable of achieving one or more, such as all, of the above.

The two biological states may comprise a benign state and a malignant state, such as of ovarian cancer.

The classification model may be capable of achieving a sensitivity of 94.92% or more. It may be capable of achieving a specificity of 82.50% or more. It may be capable of achieving a PPV of 88.89% or more. It may be capable of achieving a NPV of 91.67% or more. It may be capable of achieving an accuracy of 89.90% or more. It may be capable of achieving one or more, such as all, of the above.

The two biological states may comprise an early tumour stage and a late tumour stage, such as of ovarian cancer.

The classification model may be capable of achieving a sensitivity of 100%. It may be capable of achieving a specificity of 81.82% or more. It may be capable of achieving a PPV of 90.24% or more. It may be capable of achieving a NPV of 100%. It may be capable of achieving an accuracy of 93.22% or more. It may be capable of achieving one or more, such as all, of the above.

The method may further comprise a step (c1) between step (c) and step (d). The additional step may comprise repeating steps (b) and (c) and selecting principal components which enable optimal classification in step (c).

The method may be such that optimal classification in step (c1) is determined by assessing the output of the SVM for sensitivity, specificity and accuracy at each iteration

The classification model may further comprise (iii) the number of selected principal components enabling optimal classification in step (c).

Step (c1) may comprise retaining principal components that perform at least 55%, 65%, 75%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9% or 100% as well as the full dataset, after step (e), as assessed by any combination of sensitivity, specificity, PPV, NPV, accuracy, true negatives (TN), false negatives (FN), false positives (FP) and true positives (TP). It may comprise removing factors which do not significantly affect the performance of the SVM model. It may comprise retaining principal components whose eigenvalues are greater than or equal to 1. It may comprise retaining principal components that explain at least 55%, 65%, 75%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8% or 99.9% of the variance in the dataset. It may comprise retaining principal components that in a scree plot of eigenvalues show a smooth decrease of eigenvalues or which are to the left of a levelling off or significant decrease in gradient or elbow in the plot (scree test). Step (c1) may comprise one or more of the above. It may comprise all of the above.

The method may comprise implementing SVM using a default linear kernel. SVM_(hght) may be used.

The biological sample may comprise a serum sample of or from an individual.

The lipids may be identified by mass spectroscopy. The mass spectroscopy may comprise electrospray ionization mass spectrometry (ESI-MS). The lipids may be quantified by multiple reaction monitoring (MRM). The lipids may be identified and quantified as set out above.

The concentration of each lipid may be normalized by obtaining

${{Lipid}_{i} = \frac{x_{i}}{\lbrack{Std}\rbrack \cdot {\sum\limits_{i = 1}^{n}x_{i}}}},$

where x_(i) is the intensity of a lipid_(i) and Std is the ratio of the intensity to the amount in pmoles of a lipid standard.

There is provided, according to a 2^(nd) aspect of the present invention, a classification model obtained by a method according to the 1^(st) aspect of the invention.

We provide, according to a 3^(rd) aspect of the present invention, a classification model capable of distinguishing between a normal sample and an ovarian cancer sample. The classification model may comprise: (a) a 340×340 transformation matrix as shown in Appendix B1 and an SVM model as shown in Appendix C1; (b) a 340×85 transformation matrix comprising the first 85 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C4; (c) a 340×10 transformation matrix comprising the first 10 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C7; (d) a 82×82 transformation matrix as shown in Appendix B4 and an SVM model as shown in Appendix C10; or (e) a 77×77 transformation matrix as shown in Appendix B7 and an SVM model as shown in Appendix C13.

As a 4^(th) aspect of the present invention, there is provided a classification model capable of distinguishing between a benign cancer sample and an malignant cancer sample. The classification model may comprise: (a) a 340×340 transformation matrix as shown in Appendix B2 and an SVM model as shown in Appendix C2; (b) a 340×87 transformation matrix comprising the first 87 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C5; (c) a 340×29 transformation matrix comprising the first 29 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C8; (d) a 82×82 transformation matrix as shown in Appendix B5 and an SVM model as shown in Appendix C11; or (e) a 77×77 transformation matrix as shown in Appendix B8 and an SVM model as shown in Appendix C14.

We provide, according to a 5^(th) aspect of the present invention, a classification model capable of distinguishing between an early stage cancer sample and a late stage cancer sample. The classification model comprise (a) a 340×340 transformation matrix as shown in Appendix B3 and an SVM model as shown in Appendix C3; (b) a 340×44 transformation matrix comprising the first 44 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C6; (c) a 340×82 transformation matrix comprising the first 82 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C9; (d) a 82×82 transformation matrix as shown in Appendix B6 and an SVM model as shown in Appendix C12; or (e) a 77×77 transformation matrix as shown in Appendix B9 and an SVM model as shown in Appendix C15.

The present invention, in a 6^(th) aspect, provides a computer readable medium comprising a classification model as set out above.

In a 7^(th) aspect of the present invention, there is provided a method of determining the status of a sample, the method comprising (i) providing a dataset comprising concentrations of a plurality of lipids in the sample and (ii) applying a classification model as set out above.

The plurality of lipids may include a plurality of choline lipids, such as phosphatidylcholine (GPCho) or sphingomyelin (SM) or both. It may further optionally comprises phosphatidic acid (GPA), phosphatidylglycerol (GPGro), phosphatidylserine acid (GPSer), sulfatides, cardiolipin, phosphatidylethanolamine (GPEtn), phosphatidylinositol (GPIns), phosphatidylinositol phosphates (GPInsPs), ceramide (Cer), mono hexosyl ceramide (MonoHexCer) and di hexosyl ceramide (DiHexCer).

The plurality of lipids may comprise the lipids set out in Table D1. It may comprise the lipids set out in Table D2. It may comprise the lipids set out in Table D3. It may comprise the lipids set out in Table E4. It may comprise the lipids set out in Table E6.

The method may be used for determining whether a sample is a normal sample or an ovarian cancer sample. Such a method may comprise (i) transforming the dataset with a transformation matrix of a classification model according to the 3^(rd) aspect of the invention to generate a transformed dataset, and (ii) subjecting the transformed dataset to Support Vector Machines (SVM) analysis with an SVM model of the classification model, in which an output of >0 indicates a normal sample and an output of <0 indicates an ovarian cancer sample.

According to an 8^(th) aspect of the present invention, we provide a method of obtaining an indication useful in the diagnosis of ovarian cancer in an individual, the method comprising conducting such a method on a sample of or from a patient, in which an output of <0 indicates that the individual is suffering from ovarian cancer.

The method may be used for determining whether a sample is a benign or malignant ovarian cancer sample, the method comprising (i) transforming the dataset with a transformation matrix of a classification model according to the 4^(th) aspect of the invention to generate a transformed dataset, and (ii) subjecting the transformed dataset to Support Vector Machines (SVM) analysis with an SVM model of the classification model, in which an output of >0 indicates a benign sample and an output of <0 indicates a malignant sample.

We provide, according to a 9^(th) aspect of the invention, a method of obtaining an indication useful in the diagnosis of an individual suffering from a malignant ovarian cancer, the method comprising conducting such a method on a sample of or from a patient, in which an output of >0 indicates that the individual is suffering from a benign ovarian cancer and an output of <0 indicates that the individual is suffering from a malignant ovarian cancer.

The method may be used for determining whether a sample is an early stage cancer or a late stage ovarian cancer sample, the method comprising (i) transforming the dataset with a transformation matrix of a classification model according to the 5^(th) aspect of the invention to generate a transformed dataset, and (ii) subjecting the transformed dataset to Support Vector Machines (SVM) analysis with an SVM model of the classification model, in which an output of >0 indicates an early stage cancer sample and an output of <0 indicates a late stage cancer sample.

There is provided, in accordance with a 10^(th) aspect of the present invention, a method of obtaining an indication useful in the diagnosis of an individual suffering from a late stage ovarian cancer, the method comprising conducting such a method on a sample of or from a patient, in which an output of >0 indicates that the individual is suffering from an early stage ovarian cancer and an output of <0 indicates that the individual is suffering from a late stage ovarian cancer.

As an 11^(th) aspect of the invention, we provide a method of detecting, and optionally classing, ovarian cancer in an individual, the method comprising: (a) providing a dataset comprising concentrations of the lipids set out in Table D1, Table D2, Table D3, Table E4 or Table E6 in a sample from or of the individual; (b) transforming the dataset with a transformation matrix of a classification model according to the 3^(rd) aspect of the invention to generate a transformed dataset, and subjecting the transformed dataset to Support Vector Machines (SVM) analysis with an SVM model of the classification model, in which an output of >0 indicates a normal sample and an output of <0 indicates an ovarian cancer sample; and (c) in the case of the latter, further transforming the dataset with a transformation matrix of a classification model according to the 4^(th) aspect of the invention to generate a transformed dataset and subjecting the transformed dataset to Support Vector Machines (SVM) analysis with an SVM model of the classification model; in which an output of <0 indicates a benign sample and an output of <0 indicates a malignant sample; (d) in the case of the latter, further transforming the dataset with a transformation matrix of a classification model according to the 5^(th) aspect of the invention to generate a transformed dataset and subjecting the transformed dataset to Support Vector Machines (SVM) analysis with an SVM model of the classification model; in which an output of >0 indicates an early stage sample and an output of <0 indicates a late stage sample.

The methods described above may be implemented as computer implemented methods.

We provide, according to a 12^(th) aspect of the invention, there is provided a method of treatment or prevention of cancer, such as ovarian cancer, in an individual, the method comprising any one or more of detecting or diagnosing the cancer and optionally classing the cancer, in an individual by a method as set out above, and administering a suitable treatment or prophylactic, such as a drug known or suspected to be useful for treating cancer, to the individual.

According to a 13^(th) aspect of the present invention, we provide a method of generating a classification model capable of distinguishing between two biological states, the method comprising the steps of: (a) providing a training dataset, X, comprising concentrations of a plurality of lipids in a biological sample in a first state and a biological sample in a second state; (b) subjecting the training dataset X to Principal Components Analysis (PCA) to generate a transformation matrix, C, comprising principal component coefficients; and a representation, Y, of the dataset X in the principal component space; (c) forming an input vector, Y_(l), comprising the l most significant row vectors of Y; (d) subjecting the input vector Y_(l) to Support Vector Machines (SVM) analysis; (e) repeating steps (c) and (d) with varying l to determine a minimum dimension, l_(min), of the principal component space sufficient to obtain optimal classification; (f) forming a classification model comprising (i) the transformation matrix C, (ii) the minimum dimension l_(min), and (ii) the SVM model comprising SVM weights corresponding to the minimum dimension l_(min).

There is provided, according to a 14^(th) aspect of the present invention, a combination of lipids selected from the group consisting of: (a) lipids shown in Table D1; (b) lipids shown in Table D2; (c) lipids shown in Table D3; (d) lipids shown in Table E4; and (e) lipids shown in Table E6.

We provide, according to a 15^(th) aspect of the present invention, a combination of lipids comprising two or more, such as at least 5, lipids selected from Table D1, Table D2, Table D3, Table E4 or Table E6.

We provide, according to a 16^(th) aspect of the present invention, a classification model selected from the group consisting of: (a) a classification model capable of distinguishing between a normal sample and a diseased (ovarian cancer) sample comprising: (i) an n×m transformation matrix, where n<340 and 1≦m≦n, comprising the first m columns of the matrix shown in Appendix B1; and (ii) an SVM model generated from applying SVM analysis on a transformed dataset, the transformed dataset being generated by transforming a dataset comprising concentrations of the first n lipids shown in Table D3 in a normal sample and a diseased (ovarian cancer) sample with an n×m transformation of (a)(i); (b) a classification model capable of distinguishing between a benign sample and a malignant sample comprising: (i) an n×m transformation matrix, where n<340 and 1≦m≦n, comprising the first m columns of the matrix shown in Appendix B2; and (ii) an SVM model generated from applying SVM analysis on a transformed dataset, the transformed dataset being generated by transforming a dataset comprising concentrations of the first n lipids shown in Table D3 in a benign sample and a malignant sample with an n×m transformation matrix of (b)(i); (c) a classification model capable of distinguishing between an early stage sample and a late stage sample comprising: (i) an n×m transformation matrix, where n<340 and 1≦m≦n, comprising the first m columns of the matrix shown in Appendix B3; and (ii) an SVM model generated from applying SVM analysis on a transformed dataset, the transformed dataset being generated by transforming a dataset comprising concentrations of the first n lipids shown in Table D3 in an early stage sample and a late stage sample with an n×m transformation matrix of (c)(i).

The practice of the present invention will employ, unless otherwise indicated, conventional techniques of chemistry, molecular biology, microbiology, recombinant DNA and immunology, which are within the capabilities of a person of ordinary skill in the art. Such techniques are explained in the literature. See, for example, J. Sambrook, E. F. Fritsch, and T. Maniatis, 1989, Molecular Cloning: A Laboratory Manual, Second Edition, Books 1-3, Cold Spring Harbor Laboratory Press; Ausubel, F. M. et al. (1995 and periodic supplements; Current Protocols in Molecular Biology, ch. 9, 13, and 16, John Wiley & Sons, New York, N.Y.); B. Roe, J. Crabtree, and A. Kahn, 1996, DNA Isolation and Sequencing: Essential Techniques, John Wiley & Sons; J. M. Polak and James O'D. McGee, 1990, In Situ Hybridization: Principles and Practice; Oxford University Press; M. J. Gait (Editor), 1984, Oligonucleotide Synthesis: A Practical Approach, Irl Press; D. M. J. Lilley and J. E. Dahlberg, 1992, Methods of Enzymology: DNA Structure Part A: Synthesis and Physical Analysis of DNA Methods in Enzymology, Academic Press; Using Antibodies: A Laboratory Manual: Portable Protocol NO. I by Edward Harlow, David Lane, Ed Harlow (1999, Cold Spring Harbor Laboratory Press, ISBN 0-87969-544-7); Antibodies: A Laboratory Manual by Ed Harlow (Editor), David Lane (Editor) (1988, Cold Spring Harbor Laboratory Press, ISBN 0-87969-314-2), 1855. Handbook of Drug Screening, edited by Ramakrishna Seethala, Prabhavathi B. Fernandes (2001, New York, N.Y., Marcel Dekker, ISBN 0-8247-0562-9); and Lab Ref: A Handbook of Recipes, Reagents, and Other Reference Tools for Use at the Bench, Edited Jane Roskams and Linda Rodgers, 2002, Cold Spring Harbor Laboratory, ISBN 0-87969-630-3. Each of these general texts is herein incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a chart illustrating the work flow for development of binary classifiers for ovarian cancers based on multiparameter analysis of plasma lipids and supervised learning. The flow diagram summarises the methodology for building the prediction model and diagnosis of unknown samples.

FIG. 2 is a flowchart showing the training process.

FIG. 3 illustrates the ESI profile of a representative plasma sample.

FIG. 4 illustrates the flow diagram of the samples. Case-control study setup. A total of 211 samples are randomly separated into a training set and a test set. The training set comprises 39 controls and 99 patients with 40 benign and 59 malignant forms of cancers, respectively. The model is built based on this training set and evaluated on the remaining 73 test samples.

FIG. 5 shows comparative sample lipid levels. Raw data of multiparameter lipid analysis. The levels of individual lipids in plasma samples are quantified by electrospray ionization mass spectrometry and the intensity of each pixel indicates the concentration (from 0-85 μmoles/L and 0-315 μmoles/L for lipids measured in negative and positive ionization mode, respectively). Lipid species from eleven different classes are included in this study. Phosphatidic acid (GPA), phosphatidylglycerol (GPGro), phosphatidylserine (GPSer), sulfatides (Sulf), cardiolipins (CL), phosphatidylethanolamine (GPEtn), phosphatidylinositol (PtdIns) and its singly and doubly phosphorylated derivatives (GPInsPs), phosphatidylcholine (GPCho), ceramides and their glycosyl derivatives (Cer) and sphingomyelins (SM).

FIG. 6 shows principal component analysis between different sample sets. Multivariate statistical analysis and diagnostic performance in patients with different stages of ovarian cancer. Principal components analysis between (A) and (B) control and patients, (C) and (D) benign and malignant cases, (E) and (F) early and late cases and (G) and (H) control and benign and malignant cases.

FIG. 7 shows diagnostic utility plot between different sets of sample of conditions. SVM output is represented as diagnostic utility matrixes for comparison between (A) control and patient, (B) benign and malignant forms of tumor differentiation, (C) early and late forms of tumour differentiation, (D) control/benign and malignant and (E) benign and early. The numbers represent true positive (TP), true negative (TN), false positive (FP) and false negative (FN) events in each category.

FIG. 8 shows a receiver operating characteristics (ROC) curve comparison for differences between the different diagnostic utilities.

FIG. 9 shows a receiver operating characteristics (ROC) curve comparison between the lipidomic based biomarker and the clinical biomarker CA125 for differentiation of benign samples from malignant. The dashed line and dotted lines are the individual diagnostic performances of CA-125 and lysoGPA, respectively. Note the dramatic increase in predictive performance by multiparameter lipid markers.

FIG. 10 shows a layout of the predictor model with extent of changes in the concentration of some lipids between cases and controls. Representative panel illustrating the extent of changes in the concentration of individual lipids between cases and controls. Data is represented as mean±s.e.m. and p-values obtained using the Kruskal-Wallis test.

FIG. 11 shows the contribution of individual plasma lipids in diagnosis of patients with different stages of ovarian cancer. The lipid species covered in this study are represented as a pie chart. Percentages indicate the relative distribution among the 11 classes of lipids. The outer boundary of the chart depicts lyso (open) and non-lyso forms of lipids (filled). Only lipids with a difference and p<0.001 are shown. A blue circle indicates a increase in a particular lipid in malignant over benign forms of tumor while a red diamond reflects a decrease in the respective lipid in patient over control. Note the dramatic alterations of choline lipids (GPCho and SM) in plasma from patients with ovarian cancer. Some of the lipid species are represented as chemical structures.

FIG. 12 shows a receiver operating characteristics (ROC) curve comparison between a classification model derived from analysis of the 77 choline lipids (GPCho and SM) in Table D1 (dashed line) and a classification model derived from analysis of the 340 lipids in Table D3 (solid line).

FIG. 13 is a flowchart showing the prediction process.

FIG. 14 shows the layout of the predictor model. Arrangement of individual SVMs for classification of blinded samples. SVM-1 is based on the 138 training set (patient/control). SVM-2 is based on 99 patient samples (malignant/benign). SVM-3 is based on 59 samples (early/late malignancy).

APPENDICES

Appendix A: MRM Conditions for Lipids

Appendix B1: PCA Transformation Matrix (340×340; Normal/Diseased); Appendix B2: PCA Transformation Matrix (340×340; Benign/Malignant); Appendix B3: PCA Transformation Matrix (340×340; Early/Late); Appendix B4: PCA Transformation Matrix (82×82; Normal/Diseased); Appendix B5: PCA Transformation Matrix (82×82; Benign/Malignant); Appendix B6: PCA Transformation Matrix (82×82; Early/Late); Appendix B7: PCA Transformation Matrix (77×77; Normal/Diseased); Appendix B8: PCA Transformation Matrix (77×77; Benign/Malignant); Appendix B9: PCA Transformation Matrix (77×77; Early/Late).

Appendix C1: SVM Model Weights (340; Normal/Diseased); Appendix C2: SVM Model Weights (340; Benign/Malignant); Appendix C3: SVM Model Weights (340; Early/Late); Appendix C4: SVM Model Weights (85; Normal/Diseased); Appendix C5: SVM Model Weights (87; Benign/Malignant); Appendix C6: SVM Model Weights (44; Early/Late); Appendix C7: SVM Model Weights (10; Normal/Diseased); Appendix C8: SVM Model Weights (29; Benign/Malignant); Appendix C9: SVM Model Weights (9; Early/Late); Appendix C10: SVM Model Weights (82; Normal/Diseased); Appendix C11: SVM Model Weights (82; Benign/Malignant); Appendix C12: SVM Model Weights (82; Early/Late); Appendix C13: SVM Model Weights (77; Normal/Diseased); Appendix C14: SVM Model Weights (77; Benign/Malignant); Appendix C15: SVM Model Weights (77; Early/Late).

Appendix D: Ovarian Cancer Training Dataset.

Appendix E1: Cumulative Performance of Model versus Number of Principal Components Selected (Normal vs Diseased; 340 Lipids). Appendix E2: Cumulative Performance of Model versus Number of Principal Components Selected (Benign vs Malignant; 340 Lipids). Appendix E3: Cumulative Performance of Model versus Number of Principal Components Selected (Early vs Late; 340 Lipids)

DETAILED DESCRIPTION

We disclose combinations of lipids which are predictive of a biological state of a sample. We describe various methods of determining the status of a sample, by determining the concentration of one or more, such as a plurality, of lipids in the sample.

We disclose the use of such combinations to predict the biological state of a sample. The methods include both biochemical and bioinformatic methods.

We disclose methods comprising assaying the concentration of particular lipids and combinations of lipids in biological samples as well as rules for determining from such lipid concentrations a biological state of the sample. The lipid concentrations of the combinations of the lipids may therefore be used in a “biochemical” method, as described below, to provide an indication of the state, condition or status of sample, such as a biological state.

The combinations of lipids, and concentrations thereof, may also be used in a “bioinformatics” method.

The bioinformatics methods involve applying classification models disclosed in this document to lipid concentration data obtained from unknown samples. The classification models may be generated using lipid concentration data from biological samples in known states, as described in detail below. The bioinformatics predictive methods may involve biochemical steps, and vice versa. The bioinformatics methods may comprise simply applying already generated classification models onto obtained data, or they may actually include model generation steps.

We therefore disclose classification models, methods of generating such classification models and methods of applying such classification models, which may be used bioinformatically for such predictions.

The biological state may comprise a cancerous state. It may comprise a neoplasmic state or a tumour state. The cancer may comprise ovarian cancer. The biological state may comprise a malignancy state of a cancer. The malignancy state may comprise a benign state or it may comprise a malignant state. The biological state may comprise a stage of a cancer. The stage may comprise an early stage or a late stage.

The term “ovarian cancer” as used in this document may comprise ovarian tumors, carcinomas, (e.g., carcinoma in situ, invasive carcinoma, metastatic carcinoma) and premalignant conditions. It further includes both benign and malignant tumors, such as ovarian germ cell tumors, e.g. teratomas, dysgerminoma, endodermal sinus tumor and embryonal carcinoma, and ovarian stromal tumors, e.g. granulosa, theca, Sertoli, Leydig, and collagen-producing stromal cells. Ovarian cancers also include recognized histological tumor types, such as, for example, serous, mucinous, endometrioid, and clear cell tumors.

The ovarian cancer stage may be established using for example the FIGO staging system, as described in L. Sobin and Ch Wittekind (eds.), TNM Classification of malignant tumours. UICC Internation Union against Cancer, Geneva, Switzerland, p155-157; 6th ed. 2002. “Early” stage may correspond to Stage I and Stage II. “Late” stage may correspond to Stage III and Stage IV.

The biological state may comprise one of at least two binary states. It may comprise one two biological states. The biological states may comprise or example, a diseased state (such as a cancer state) and a normal state, a benign state and a malignant state and an early stage state and a late stage state. The biological states may comprise a normal/benign state and a malignant state. The biological states may comprise a benign state and an early state.

Thus, the Examples below show that a combination of 340 lipids, as shown in Table D3, may be analysed in a sample in order to determine the status of a sample. The Examples show that the is capable of revealing whether the sample is normal or diseased (ovarian cancer). These lipids may also be used to further determine whether such a sample is a benign sample or a malignant sample. Further analysis with these lipids may be done to determine whether such a sample is an early stage sample or a late stage sample.

The methods and compositions described here involve determining lipid concentrations in a sample. The concentrations may be those of the lipids in a lipid combination disclosed herein and means of obtaining the concentrations are described in detail below.

Lipid Combinations

Our methods may involve determining lipid concentrations of a number of lipids.

The combination of lipids may comprise any two or more choline lipids, such as phosphatidylcholine (GPCho) or sphingomyelin (SM) or both. The combinations may comprise any two or more lipids selected from the group consisting of: phosphatidic acid (GPA). They may comprise phosphatidylglycerol (GPGro), phosphatidylserine acid (GPSer), sulfatides, cardiolipin, phosphatidylethanolamine (GPEtn), phosphatidylinositol (GPIns), phosphatidylinositol phosphates (GPInsPs), ceramide (Cer), mono hexosyl ceramide (MonoHexCer) and di hexosyl ceramide (DiHexCer). The combinations may comprise lipids from more than one class.

We therefore disclose combinations of lipids comprising, such as consisting of, all or substantially all of the (a) lipids shown in Table D1; (b) lipids shown in Table D2; (c) lipids shown in Table D3; (d) lipids shown in Table E4; and (e) lipids shown in Table E6. We further disclose combinations of such combinations.

We further disclose combinations of any two or more lipids from each of these tables. The two or more lipids may be contiguous or non-contiguous. For example, we disclose a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339 or 440 lipids from any of these tables, for example, Table D3.

Any number of lipids within the 340 lipids shown in Table D3 may be employed for the purposes described in this document. For example, all or substantially all of these lipids may be used. As another example, combinations comprising subsets from the 340 lipids may also be employed. Thus, a combination of any n lipids of the list of 340 lipids, where n≦340, may be used for such analysis.

Such combinations may comprise contiguous subsets or non-contiguous subsets. For example, combinations may be formed primarily from the top half of the list of Table D3. They may be formed primarily from the top third of the list, the top quarter, the top fifth, the top sixth, the top seventh, the top eighth, the top ninth or the top tenth of the list. Thus, subsets may be formed from the first 170, the first 114, the first 85, the first 68, the first 57, the first 49, the first 43 or the first 38 members of the list.

In general terms, subsets of the list of Table D3 lipids may, for example, include a certain number of lipids from top of the list of lipids shown in Table D3. Such lipid combinations may comprise the first n lipids from the list of lipids shown in Table D3. For example, n could be 82. Such a combination of lipids is shown in Table D2. A further combination of lipids could comprise the first n lipids from the list of lipids shown in Table D3, where n=44. Such a combination of lipids is shown in Table E6. Yet another combination of lipids may comprise the first 30 lipids from the list of lipids shown in Table D3 (i.e., n=30). Such a combination of lipids is shown in Table E4.

Other combinations comprising subsets of the list of 340 lipids may comprise a selection of lipids from Table D3, based on any suitable criteria, such as lipid class, molecular weight, side group, etc. For example, subsets may be formed comprising all lipids in particular lipid class from the lipids set out in Table D3. A subset may comprise choline lipids, for example. Such an example subset is shown in Table D1 (below), and comprises a list of 77 choline lipids.

Lipid nomenclature is as described in Fahy, et al (2005). A comprehensive classification system for lipids. Journal of Lipid Research, Vol. 46, 839-862.

TABLE D1 List of 77 Choline Lipids Lipid 1 494.4/184.1>GPCho:Lyso 16:1 40 810.6/184.1>GPCho:38:4a 2 496.4/184.1>GPCho:Lyso 16:0 41 812.6/184.1>GPCho:38:3a 3 520.4/184.1>GPCho:Lyso 18:2 42 814.6/184.1>GPCho:38:2a 4 522.4/184.1>GPCho:Lyso 18:1 43 816.6/184.1>GPCho:38:1a 5 524.4/184.1>GPCho:Lyso 18:0 44 820.6/184.1>GPCho:40:5p, 40:6e 6 544.4/184.1>GPCho:Lyso 20:4 45 822.6/184.1>GPCho:40:4p, 40:5e 7 568.4/184.1>GPCho:Lyso 22:6 46 824.6/184.1>GPCho:40:3p, 40:4e 8 570.4/184.1>GPCho:Lyso 22:5 47 826.6/184.1>GPCho:40:2p, 40:3e 9 678.5/184.1>GPCho:28:0 48 828.6/184.1>GPCho:40:1p, 40:2e 10 678.5/184.1>GPCho:28:0a 49 834.6/184.1>GPCho:40:6a 11 704.6/184.1>GPCho:30:1a 50 836.6/184.1>GPCho:40:5a 12 706.6/184.1>GPCho:30:0a 51 838.6/184.1>GPCho:40:4a 13 718.6/184.1>GPCho:32:0p, 32:1e 52 701.5/184.1>SM:18/16:1 14 730.8/184.1>GPCho:32:2 53 703.5/184.1>SM:18/16:0 15 732.6/184.1>GPCho:32:1a 54 703.8/184.4>SM:d18:1/16:0 16 734.6/184.1>GPCho:32:0a 55 705.8/184.4>SM:d18:0/16:0 17 742.6/184.1>GPCho:34:2p, 34:3e 56 727.6/184.1>SM:18/18:2 18 744.6/184.1>GPCho:34:1p, 34:2e 57 729.6/184.1>SM:18/18:1 19 746.6/184.1>GPCho:34:0p, 34:1e 58 731.6/184.1>SM:18/18:0 20 748.6/184.1>GPCho:34:0e 59 731.8/184.4>SM:d18:1/18:0 21 756.6/184.1>GPCho:34:3a 60 733.8/184.4>SM:d18:0/18:0 22 758.7/184.1>GPCho:34:2a 61 757.6/184.1>SM:18/20:1 23 760.6/184.1>GPCho:34:1a 62 759.6/184.1>SM:18/20:0 24 762.6/184.1>GPCho:34:0a 63 759.8/184.4>SM:d18:1/20:0 25 768.6/184.1>GPCho:36:3p, 36:4e 64 761.8/184.4>SM:d18:0/20:0 26 770.6/184.1>GPCho:36:2p, 36:3e 65 773.6/184.1>SM:18/21:0 27 772.6/184.1>GPCho:36:1p, 36:2e 66 787.6/184.1>SM:18/22:0 28 774.6/184.1>GPCho:36:0p, 36:1e 67 787.9/184.4>SM:d18:1/22:0 29 782.6/184.1>GPCho:36:4a 68 789.9/184.4>SM:d18:0/22:0 30 784.6/184.1>GPCho:36:3a 69 813.6/184.1>SM:18/24:1 31 786.6/184.1>GPCho:36:2a 70 813.9/184.4>SM:d18:1/24:1 32 788.6/184.1>GPCho:36:1a 71 815.6/184.1>SM:18/24:0 33 790.8/184.1>GPCho:36:0 72 815.9/184.4>SM:d18:0/24:1 34 792.6/184.1>GPCho:38:5p, 38:6e 73 817.9/184.4>SM:d18:0/24:0 35 794.6/184.1>GPCho:38:4p, 38:5e 74 841.9/184.4>SM:d18:1/26:1 36 796.6/184.1>GPCho:38:3p, 38:4e 75 843.9/184.4>SM:d18:0/26:1 37 798.6/184.1>GPCho:38:2p, 38:3e 76 843.9/184.4>SM:d18:1/26:0 38 800.6/184.1>GPCho:38:1p, 38:2e 77 845.9/184.4>SM:d18:0/26:0 39 808.6/184.1>GPCho:38:5a

TABLE D2 List of 82 lipids List of 82 Lipids Lipid m/z Benign Malignant p-value 1 GPA:36:0 703.8 29.71 80.76 <1.0E−06 2 GPA:16:0/22:5 721.8 65.75 187.84 <1.0E−06 3 GPA:38:0 731.8 44.66 109.80 <1.0E−06 4 GPGro:Lyso 16:0 483.4 89.99 175.45 <1.0E−06 5 GPGro:Lyso 18:0 511.4 89.22 186.02 <1.0E−06 6 GPGro:18:2/18:2 769.8 101.89 68.33 <1.0E−06 7 GPGro:18:0/18:0 777.8 57.67 175.25 <1.0E−06 8 GPEtn:Lyso18:2a 476.6 55.96 46.37 <1.0E−06 9 GPEtn:Lyso 18:1 478.4 61.14 53.12 <1.0E−06 10 GPEtn:Lyso 20:4 500.4 77.46 40.49 <1.0E−06 11 GPEtn:Lyso 22:6 524.4 96.20 51.64 <1.0E−06 12 GPCho:32:0a 734.6 106.21 125.10 <1.0E−06 13 GPCho:34:2e 744.6 81.22 65.69 <1.0E−06 14 GPCho:36:2a 786.6 90.83 81.70 <1.0E−06 15 GPCho:38:2a 814.6 103.44 127.24 <1.0E−06 16 Cer: d18:1/18:0 566.7 118.28 232.24 <1.0E−06 17 Cer: d18:1/20:0 594.7 96.63 172.62 <1.0E−06 18 Cer: d18:1/22:0 622.8 74.18 122.63 <1.0E−06 19 Cer: d18:1/24:1 648.9 84.79 167.95 <1.0E−06 20 SM: d18:1/18:0 731.8 120.26 154.36 <1.0E−06 21 SM: d18:1/22:0 787.9 89.88 88.01 <1.0E−06 22 SM: d18:1/24:1 813.9 106.69 132.41 <1.0E−06 23 GPA:36:1 701.8 15.82 47.90 1.0E−06 24 GPCho:36:3a 784.6 93.47 79.88 1.0E−06 25 GPCho:40:5p, 40:6e 820.6 126.80 91.35 1.0E−06 26 GPCho:40:6a 834.6 137.30 94.11 1.0E−06 27 SM: d18:1/26:0 843.9 135.42 92.25 1.0E−06 28 GPCho:Lyso 18:2 520.4 78.55 75.98 2.0E−06 29 GPCho:38:5a 808.6 117.55 86.87 3.0E−06 30 GPA:18:1/16:0 673.8 34.06 71.35 4.0E−06 31 Cer: d18:1/24:0 650.9 58.15 91.39 5.0E−06 32 GPCho:38:5p, 38:6e 792.6 113.63 89.05 6.0E−06 33 DiHexCer: d18:1/18:0 890.7 93.33 143.80 7.0E−06 34 GPGro:18:2/18:1 771.8 75.18 77.63 1.0E−05 35 GPCho:36:2p, 36:3e 770.6 93.96 80.83 1.0E−05 36 GPA:36:2 699.8 41.77 76.41 1.1E−05 37 GPCho:28:0a 678.5 62.22 69.74 1.2E−05 38 MonoHexCer: d18:1/18:0 728.7 78.23 130.92 1.3E−05 39 MonoHexCer: d18:1/24:1 810.9 96.74 147.14 1.9E−05 40 Cer: d18:1/16:0 538.7 109.71 152.16 2.6E−05 41 GPCho:32:1a 732.6 106.83 121.94 3.3E−05 42 GPGro:18:2/18:0 773.8 78.31 91.50 3.5E−05 43 GPCho:36:3p, 36:4e 768.6 107.07 84.53 5.0E−05 44 GPGro:18:1/18:0 775.8 97.92 131.80 8.4E−05 45 SM:d18:1/16:0 703.8 106.53 119.97 1.1E−04 46 GPA:Lyso 18:0 437.4 58.55 92.21 1.4E−04 47 GPCho:38:4p, 38:5e 794.6 119.84 95.63 1.8E−04 48 MonoHexCer: d18:1/16:0 700.7 100.99 133.15 1.9E−04 49 GPCho:Lyso 22:6 568.4 125.53 89.80 1.9E−04 50 GPCho:40:1p, 40:2e 828.6 96.59 128.57 2.1E−04 51 DiHexCer: d18:1/24:1 972.9 94.84 141.35 6.6E−04 52 SM: d18:1/26:1 841.9 92.64 128.68 7.0E−04 53 GPGro:Lyso 18:2 507.4 81.41 123.68 9.7E−04 54 GPCho:30:1a 704.6 102.55 116.54 1.1E−03 55 GPCho:38:3p, 38:4e 796.6 114.76 96.77 1.3E−03 56 GPCho:Lyso 22:5 570.4 111.54 87.89 1.4E−03 57 GPCho:36:4a 782.6 109.60 91.61 1.5E−03 58 GPA:40:0 759.8 61.30 93.59 2.0E−03 59 GPCho:38:4a 810.6 114.05 94.41 2.4E−03 60 GPCho:40:2p, 40:3e 826.6 87.13 122.72 2.4E−03 61 GPA:40:1 757.8 82.86 102.69 2.4E−03 62 GPA:18:0/20:4 723.8 64.99 86.23 2.6E−03 63 GPCho:34:3a 756.6 78.28 107.87 3.0E−03 64 DiHexCer: d18:1/24:0 974.9 90.17 119.47 3.5E−03 65 GPGro:Lyso 18:1 509.4 126.58 136.56 3.7E−03 66 GPCho:34:2a 758.7 102.25 93.80 3.7E−03 67 GPCho:36:1a 788.6 88.10 93.30 3.9E−03 68 GPCho:34:0e 748.6 100.65 109.00 4.8E−03 69 GPGro:18:2/16:1 743.8 98.53 88.80 5.1E−03 70 GPCho:38:1a 816.6 89.63 102.87 8.0E−03 71 DiHexCer: d18:1/22:0 946.8 92.28 122.15 8.7E−03 72 MonoHexCer: d18:1/22:0 784.8 89.45 107.59 9.7E−03 73 GPCho:36:1p, 36:2e 772.6 87.79 88.82 1.2E−02 74 MonoHexCer: d18:1/24:0 812.9 90.45 102.49 1.2E−02 75 DiHexCer: d18:1/16:0 862.7 91.93 115.85 1.4E−02 76 GPEtn:Lyso 18:0 480.4 89.30 104.93 1.5E−02 77 GPA:16:0/16:0 647.8 76.90 108.70 1.6E−02 78 GPCho:34:2p, 34:3e 742.6 79.46 82.38 1.6E−02 79 GPCho:34:0a 762.6 101.35 109.61 3.2E−02 80 GPCho:Lyso 20:4 544.4 102.40 89.08 3.3E−02 81 GPCho:40:5a 836.6 111.74 97.42 4.1E−02 82 GPA:Lyso 16:0 409.4 93.86 115.94 4.7E−02

TABLE D3 List of 340 choline lipids List of 340 Lipids No. Lipid 1 GPA:Lyso 16:0 2 GPA:Lyso 18:2 3 GPA:Lyso 18:1 4 GPA:Lyso 18:0 5 GPA:Lyso 18:0 6 GPA:Lyso 20:3 7 GPA:Lyso 20:2 8 GPA:Lyso 20:1 9 GPA:Lyso 20:0 10 GPA:Lyso 22:6 11 GPA:Lyso 22:5 12 GPA:16:1/16:2 13 GPA:16:1/16:1 14 GPA:16:1/16:0 15 GPA:16:0/16:0 16 GPA:34:4 17 GPA:34:3 18 GPA:34:4 19 GPA:18:2/16:0 20 GPA:18:1/16:0 21 GPA:36:4 22 GPA:36:4 23 GPA:20:3/16:0 24 GPA:18:1/18:2 25 GPA:36:2 26 GPA:36:2 27 GPA:36:1 28 GPA:36:0 29 GPA:18:1/20:4 30 GPA:16:0/22:5 31 GPA:18:0/20:4 32 GPA:20:3/18:0 33 GPA:38:1 34 GPA:38:0 35 GPA:40:4 36 GPA:40:1 37 GPA:40:0 38 GPA:42:5 39 GPGro:Lyso 16:1 40 GPGro:Lyso 16:0 41 GPGro:Lyso 18:2 42 GPGro:Lyso 18:1 43 GPGro:Lyso 18:0 44 GPGro:Lyso 20:4 45 GPGro:Lyso 22:6 46 GPGro:Lyso 22:5 47 GPGro:16:1/16:1 48 GPGro:16:1/16:0 49 GPGro:16:0/16:0 50 GPGro:18:2/16:1 51 GPGro:18:1/16:2 52 GPGro:18:2/16:0 53 GPGro:18:1/16:1 54 GPGro:16:0/18:1 55 GPGro:16:0/18:1 56 GPGro:18:0/16:0 57 GPGro:20:4/16:1 58 GPGro:18:2/18:2 59 GPGro:20:4/16:0 60 GPGro:18:2/18:1 61 GPGro:18:2/18:0 62 GPGro:18:1/18:1 63 GPGro:18:1/18:0 64 GPGro:18:0/18:0 65 GPGro:20:4/18:1 66 GPGro:20:4/18:0 67 GPGro:22:6/18:0 68 GPGro:22:5/18:0 69 GPSer:Lyso 16:1 70 GPSer:Lyso 16:0 71 GPSer:Lyso 18:1 72 GPSer:Lyso 18:0 73 GPSer:Lyso 20:4 74 GPSer:Lyso 22:5 75 GPSer:32:1 76 GPSer:32:0 77 GPSer:34:2 78 GPSer:34:1 79 GPSer:34:0 80 GPSer:36:4 81 GPSer:36:3 82 GPSer:36:2 83 GPSer:36:1 84 GPSer:36:0 85 GPSer:38:6 86 GPSer:38:5 87 GPSer:38:4 88 GPSer:38:3 89 GPSer:38:2 90 GPSer:38:1 91 GPSer:40:6 92 GPSer:40:5 93 GPSer:40:4 94 GPSer:40:3 95 Sulfatide:16:0 96 Sulfatide:18:0 97 Sulfatide:18:0 (OH) 98 Sulfatide:20:0 99 Sulfatide:20:0 (OH) 100 Sulfatide:22:1 101 Sulfatide:22:1 (OH) 102 Sulfatide:24:1 103 Sulfatide:24:0 104 Sulfatide:24:0 (OH) 105 Cardiolipin:52:3 106 Cardiolipin:66:2 107 Cardiolipin:68:4 108 Cardiolipin:68:3 109 Cardiolipin:68:2 110 Cardiolipin:68:1 111 Cardiolipin:70:5 112 Cardiolipin:70:4 113 Cardiolipin:70:3 114 Cardiolipin:70:2 115 Cardiolipin:70:l 116 Cardiolipin:70:0 117 GPEtn:Lyso16:1e/16:0p 118 GPEtn:Lyso 16:1 119 GPEtn:Lyso 16:0 120 GPEtn:Lyso18:2e/18:1p 121 GPEtn:Lyso18:1e/18:0p 122 GPEtn:Lyso18:2a 123 GPEtn:Lyso 18:1 124 GPEtn:Lyso 18:0 125 GPEtn:Lyso20:1e/20:0p 126 GPEtn:Lyso 20:4 127 GPEtn:Lyso 22:6 128 GPEtn:16:0/16:1 129 GPEtn:16:0/16:0 130 GPEtn:34:2p, 34:3e 131 GPEtn:34:1p, 34:2e 132 GPEtn:34:0p, 34:1e 133 GPEtn:18:1/16:1 134 GPEtn:18:2/16:1 135 GPEtn:18:1/16:1 136 GPEtn:18:1/16:0 137 GPEtn:18:0/16:0 138 GPEtn:36:4p 139 GPEtn:36:3p, 36:4e 140 GPEtn:36:2p, 36:3e 141 GPEtn:36:1p, 36:2e 142 GPEtn:20:4/16:0 143 GPEtn:18:2/18:1 144 GPEtn:18:1/18:1 145 GPEtn:18:0/18:1 146 GPEtn:18:0/18:0 147 GPEtn:38:5p, 38:6e 148 GPEtn:38:4p, 38:5e 149 GPEtn:38:3p, 38:4e 150 GPEtn:38:2p, 38:3e 151 GPEtn:38:1p, 38:2e 152 GPEtn:20:4/18:2 153 GPEtn:20:4/18:1 154 GPEtn:20:4/18:0 155 GPEtn:20:3/18:0 156 GPEtn:20:2/18:0 157 GPEtn:20:1/18:0 158 GPEtn:40:5p, 40:6e 159 GPEtn:40:4p, 40:5e 160 GPEtn:40:3p, 40:4e 161 GPEtn:40:1p, 40:2e 162 GPEtn:22:4/18:3 163 GPEtn:22:4/18:2 164 GPEtn:40:5a 165 GPEtn:40:4a 166 GPEtn:40:3a 167 GPEtn:40:2a 168 GPIns:Lyso 16:1 169 GPIns:Lyso 16:0 170 GPIns:Lyso 18:2 171 GPIns:Lyso 18:1 172 GPIns:Lyso 18:0 173 GPIns:Lyso 20:4 174 GPIns:Lyso 20:3 175 GPIns:Lyso 20:2 176 GPIns:Lyso 20:1 177 GPIns:Lyso 20:0 178 GPIns:Lyso 24:2 179 GPIns:34:1 180 GPIns:34:1 181 GPIns:34:1 182 GPIns:36:4 183 GPIns:36:3 184 GPIns:36:2 185 GPIns:18:0/18:1 186 GPIns:36:0 187 GPIns:37:3 188 GPIns:38:5 189 GPIns:38:4 190 GPIns:38:3 191 GPIns:38:2 192 GPIns:38:1 193 GPIns:38:0 194 GPIns:40:6 195 GPIns:40:5 196 GPIns:40:4 197 GPIns:40:3 198 GPIns:40:2 199 GPIns:40:1 200 GPInsP:38:5 201 GPInsP:38:5 202 GPInsP:38:4 203 GPInsP:38:4 204 GPInsP:38:3 205 GPInsP:38:3 206 GPInsP2:38:4 207 GPInsP2:38:4 208 GPInsP2:38:4 209 GPInsP2:38:3 210 GPInsP2:38:3 211 GPInsP2:38:3 212 GPInsP3:38:4 213 GPInsP3:38:4 214 GPInsP3:38:4 215 GPInsP3:38:4 216 GPCho:Lyso 16:1 217 GPCho:Lyso 16:0 218 GPCho:Lyso 18:2 219 GPCho:Lyso 18:1 220 GPCho:Lyso 18:0 221 GPCho:Lyso 20:4 222 GPCho:Lyso 22:6 223 GPCho:Lyso 22:5 224 GPCho:28:0 225 GPCho:28:0a 226 GPCho:30:1a 227 GPCho:30:0a 228 GPCho:32:0p, 32:1e 229 GPCho:32:2 230 GPCho:32:1a 231 GPCho:32:0a 232 GPCho:34:2p, 34:3e 233 GPCho:34:1p, 34:2e 234 GPCho:34:0p, 34:1e 235 GPCho:34:0e 236 GPCho:34:3a 237 GPCho:34:2a 238 GPCho:34:1a 239 GPCho:34:0a 240 GPCho:36:3p, 36:4e 241 GPCho:36:2p, 36:3e 242 GPCho:36:1p, 36:2e 243 GPCho:36:0p, 36:1e 244 GPCho:36:4a 245 GPCho:36:3a 246 GPCho:36:2a 247 GPCho:36:1a 248 GPCho:36:0 249 GPCho:38:5p, 38:6e 250 GPCho:38:4p, 38:5e 251 GPCho:38:3p, 38:4e 252 GPCho:38:2p, 38:3e 253 GPCho:38:1p, 38:2e 254 GPCho:38:5a 255 GPCho:38:4a 256 GPCho:38:3a 257 GPCho:38:2a 258 GPCho:38:1a 259 GPCho:40:5p, 40:6e 260 GPCho:40:4p, 40:5e 261 GPCho:40:3p, 40:4e 262 GPCho:40:2p, 40:3e 263 GPCho:40:1p, 40:2e 264 GPCho:40:6a 265 GPCho:40:5a 266 GPCho:40:4a 267 SM:18/16:1 268 SM:18/16:0 269 SM:d18:1/16:0 270 SM:d18:0/16:0 271 SM:18/18:2 272 SM:18/18:1 273 SM:18/18:0 274 SM:d18:1/18:0 275 SM:d18:0/18:0 276 SM:18/20:1 277 SM:18/20:0 278 SM:d18:1/20:0 279 SM:d18:0/20:0 280 SM:18/21:0 281 SM:18/22:0 282 SM:d18:1/22:0 283 SM:d18:0/22:0 284 SM:18/24:1 285 SM:d18:1/24:1 286 SM:18/24:0 287 SM:d18:0/24:1 288 SM:d18:0/24:0 289 SM:d18:1/26:1 290 SM:d18:0/26:1 291 SM:d18:1/26:0 292 SM:d18:0/26:0 293 Cer:d18:1/16:0 294 Cer:d18:0/16:0 295 Cer:d18:1/18:0 296 Cer:d18:0/18:0 297 Cer:d18:1/20:0 298 Cer:d18:0/20:0 299 Cer:d18:1/22:0 300 Cer:d18:0/22:0 301 Cer:d18:1/24:1 302 Cer:d18:1/24:0 303 Cer:d18:0/24:1 304 Cer:d18:0/24:0 305 Cer:d18:1/26:1 306 Cer:d18:1/26:0 307 Cer:d18:0/26:1 308 Cer:d18:0/26:0 309 MonoHexCer:d18:1/16:0 310 MonoHexCer:d18:0/16:0 311 MonoHexCer:d18:1/18:0 312 MonoHexCer:d18:0/18:0 313 MonoHexCer:d18:1/20:0 314 MonoHexCer:d18:0/20:0 315 MonoHexCer:d18:1/22:0 316 MonoHexCer:d18:0/22:0 317 MonoHexCer:d18:1/24:1 318 MonoHexCer:d18:0/24:1 319 MonoHexCer:d18:1/24:0 320 MonoHexCer:d18:0/24:0 321 MonoHexCer:d18:1/26:1 322 MonoHexCer:d18:0/26:1 323 MonoHexCer:d18:1/26:0 324 MonoHexCer:d18:0/26:0 325 DiHexCer:d18:1/16:0 326 DiHexCer:d18:0/16:0 327 DiHexCer:d18:1/18:0 328 DiHexCer:d18:0/18:0 329 DiHexCer:d18:1/20:0 330 DiHexCer:d18:0/20:0 331 DiHexCer:d18:1/22:0 332 DiHexCer:d18:0/22:0 333 DiHexCer:d18:1/24:1 334 DiHexCer:d18:0/24:1 335 DiHexCer:d18:1/24:0 336 DiHexCer:d18:0/24:0 337 DiHexCer:d18:1/26:1 338 DiHexCer:d18:0/26:1 339 DiHexCer:d18:1/26:0 340 DiHexCer:d18:0/26:0

Lipid Concentration Determination

Any suitable method of determining the concentration of a lipid in a sample may be carried out as known in the art.

The samples may comprise, for example, bodily fluids, such as blood, blood serum, synoival fluid, tissue lysates, and extracts prepared from tissues.

For example, any of a number of known methods for lipid concentration determination such as involving colorimetric or fluorimetric methods, may be used.

Such methods are described for example in European Patent EP0531933 and at http://www-unix.oit.umass.edu/˜mcclemen/581Lipids.html, in Holman et al. (1964) Am J Clin Nutr, 14 (4): 193, Nogueira et al (2007), Critical Care, 11(Suppl 3):P19, etc. Antonis et al (1967). Automated techniques in serum lipid analysis. Journal of the American Oil Chemists' Society, 44, 6, 333-340 describes in detail methods of determining lipid concentrations in serum samples. A number of kits for determination of lipid concentrations are also commercially available.

For choline lipids, a number of assays as described in detail in the section “Assays for Choline Lipids” may be employed. Lipid concentration determination methods may involve high performance liquid chromatography (HPLC), as described in Patton et al (1982).

The lipid concentration determination may be conducted using mass spectrometry. For example, electrospray ionization-mass spectrometry (ESI-MS) may be used. ESI-MS is described in detail in Kerwin et al (1994), Kim et al (1994), Han et al (1994), Myher et al (1995), Han et al (1996), Brügger et al (1997), Ramanadham et al (1998) Schneiter et al (1999), Hsu et al (2000), Koivusaloa et al (2001).

Assays for Choline Lipids

Assays for choline lipids, phosphatidylcholine and sphingomyelin are known in the art and may be applied to determine the concentrations of for example the 77 choline lipids shown in Table D1.

An assay for phosphatidylcholine is described in detail in Hojjati, M. R., Jiang, X. Rapid, specific, and sensitive measurements of plaSphingomyelina sphingomyelin and phosphatidylcholine. J Lipid Res 47(3) 673-676 (2006). In this assay, PC-specific PLD is first used to hydrolyze PC to choline and phosphatidic acid. The newly formed choline is then used to generate hydrogen peroxide in a reaction catalyzed by choline oxidase. Finally, with peroxidase as a catalyst, hydrogen peroxide reacts with DAOS and 4-aminoantipyrine to generate a blue dye with an optimal absorption at 595 nm. A kit for performing such an assay is commercially available from Cayman (Catalogue Number: 10009926) and Echelon (Catalogue Number: K-31PC).

An assay for sphingomyelin is described in detail in Hojjati, M. R., Jiang, X. Rapid, specific, and sensitive measurements of plaSphingomyelina sphingomyelin and phosphatidylcholine. J Lipid Res 47(3) 673-676 (2006). In this assay, sphingomyelinase is first used to hydrolyze SM to phosphorylcholine and ceramide. Alkaline phosphatase then generates choline from the phosphorylcholine and the newly formed choline is used to generate hydrogen peroxide in a reaction catalyzed by choline oxidase. Finally, with peroxidase as a catalyst, hydrogen peroxide reacts with DAOS and 4-aminoantipyrine to generate a blue color with an optimal absorption at 595 nm. A kit for performing such an assay is commercially available from Cayman (Catalogue Number: 10009928) and Echelon (Catalogue Number: K-31SM).

A assay kit capable of assaying both Phosphatidylcholine and Sphingomyelin is commercially available from Echelon (Catalogue Number: K-3100).

Choline lipids may be assayed by means of a immunoassays. A kit for performing such an assay is commercially available from ARUP Laboratory (Catalogue Number: 51590).

Enzyme assays for Phosphatidylcholine and Sphingomyelin are described in detail in Blaton V, de Buyzer M, Spincemaille J and Declercq B (1983). Enzymic assay for Phosphatidylcholine and Sphingomyelin in serum. Clinical Chemistry. 29: 806-809. An enzyme assay for quantifying Sphingomyelin is described in detail in He et al. (2001). An enzymatic assay for quantifying Sphingomyelin in tissues and plaSphingomyelina from humans and mice with Niemann-Pick Disease. Analytical Biochemistry. 2(15). 204-211.

Choline lipids may be assayed by means of thin layer chromatography. A TLC based assay is described in detail in Tsai et al. (1987). Assay of disaturated phosphatidylcholine in amniotic fluid as a test of fetal lung maturity: experience with 2000 analyses. Clinical chemistry. 33(9). 1648-1651. In this assay, crude lipid extract is separated by thin layer chromatography and PC is identified by mobility. Another TLC based assay is described in detail in Shimojo T, Abe M, Ohta M (1974). A method for determination of saturated phosphatidylcholine. Journal of Lipid Research. 15.525-527.

Choline lipids may be assayed by means of thin layer chromatography followed by GC-MS for fatty acid analysis. This is described in detail in Kahn et al. (1995). Phosphatidylcholine molecular species of calf lung surfactant. Am J Physol Lung Cell Mol Physiol. 269. 567-573. Crude lipid extract is separated by thin layer chromatography and PC is identified by mobility. Further characterization of molecular species is performed by gas chromatography mass spectrometry.

Choline lipids may be assayed by means of gas chromatography mass spectrometry. This is described in detail in Miller et al. (1991). Differences in red blood cell choline and lipid-bound choline between patients with Alzheimer disease and control subjects. Neurobiology of Aging. 12(1). 61-64.

Choline lipids may be assayed by means of MALDI-MS. This is described in detail in Shelley et al. (2005). Direct profiling of lipid distribution in brain tissue using MALDI-TOFMS. Analytical Chemistry. 77(14). 4523-4527. MS characterisation and separation of PC, ionization source is distinct from what we used (which is electrospray ionization).

Prediction Using Lipid Concentrations

The concentrations of the disclosed combinations of lipids in biological samples may be used in predictive processes to determine the state of a sample. The predictive method may provide an indication of the state, condition or status of sample. It may provide an indication of the biological state, as described above.

Various methods for achieving this, including bioinformatic and non-bioinformatic methods, are disclosed in detail below.

The Examples show that ovarian cancer samples display a reduction in levels of choline lipids, phosphatidylcholine (GPCho) and sphingomyelins (SM) compared to normal, undiseased, samples. Reduced levels of such choline lipids are also found in malignant samples compared to benign samples, and also late stage samples compared to early stage samples.

Accordingly, we disclose a method of identifying a cancerous sample, such as an ovarian cancer sample, the method comprising detecting a reduced level of a phosphatidylcholine (GPCho) or a sphingomyelin (SM), or both, in or of the sample. We further disclose methods to identify malignant samples from benign samples, and early stage samples from late stage samples, by detecting a reduced level of a phosphatidylcholine (GPCho) or a sphingomyelin (SM), or both, in or of the sample; in which a reduced level indicates that the sample is a malignant sample or a late stage sample, as the case may be. The reduced level or concentration may comprise a significantly reduced level or concentration.

Such a method may also be used for diagnosis of a cancer such as ovarian cancer, or the diagnosis of a malignant cancer, or the diagnosis of a late stage cancer, in an individual, the method comprising detecting a reduced level of a phosphatidylcholine (GPCho) or a sphingomyelin (SM), or both, in or of the sample of or from an individual.

The phosphatidylcholine (GPCho) or a sphingomyelin (SM) detected in such methods may be chosen from any one or more of the choline lipids shown in Table D1. For example, the concentrations of more than one choline lipid, for example several choline lipids such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76 or 77 lipids of Table D1 may be generated. The first such number of lipids from the top of the table may be used for this purpose.

The Examples also show that ovarian cancer samples display an increase in levels of ceramides (Cer) and glycosylated ceramides (HexCer). Increased levels of ceramides (Cer) and glycosylated ceramides (HexCer) are also found in malignant samples compared to benign samples, and also late stage samples compared to early stage samples.

Accordingly, we disclose a method of identifying a cancerous sample, such as an ovarian cancer sample, the method comprising detecting an increased level of a ceramide (Cer) or a glycosylated ceramide (HexCer), or both, in or of the sample. We further disclose methods to identify malignant samples from benign samples, and early stage samples from late stage samples, by detecting an increased level of a ceramide (Cer) or a glycosylated ceramide (HexCer), or both, in or of the sample; in which a reduced level indicates that the sample is a malignant sample or a late stage sample, as the case may be.

Such a method may also be used for diagnosis of a cancer such as ovarian cancer, or the diagnosis of a malignant cancer, or the diagnosis of a late stage cancer, in an individual, the method comprising detecting an increased level of a ceramide (Cer) or a glycosylated ceramide (HexCer), or both, in or of the sample of or from an individual. The increased level or concentration may comprise a significantly increased level or concentration.

The concentration or level of a lipid in a patient or a sample therefrom or thereof may be considered higher or lower than the normal concentration or level of a lipid if the concentration or level of a lipid is greater or less, respectively, than the normal level by an amount greater than the standard error of the assay employed to assess concentration or level of the lipid, such as at least twice, three, four, five or ten times that amount. Alternately, concentration or level of a lipid in the patient or a sample therefrom or thereof may be considered higher or lower than the normal level if the concentration or level of a lipid is at least about two, such as at least about three, four, or five times, higher or lower, respectively, than the normal concentration or level of a lipid.

The phosphatidylcholine (GPCho) or a sphingomyelin (SM) detected in such methods may be chosen from any one or more of the lipids shown in the relevant section of Table D3.

Generation of Classification Models

The predictive method may comprise applying a classification model generated from bioinformatic analysis of samples in known states. The classification model may be generated by the methods described in this section and below.

As described above, the concentrations of combinations of lipids may be generated from samples which are in “known” states. Such samples are therefore known to be in particular biological states, e.g., diseased, cancerous, tumorous, neoplastic, benign, malignant, early stage, late stage, etc. For classing such samples, known methods may be applied, including as histological means or biochemical means. For example, ovarian cancers may be classed by Trans-vaginal Ultrasonography (TVU), by assaying serum CA 125 levels or examination of biopsies. The samples may be staged by methods known in the art.

A classification model capable of distinguishing two biological states may then be built by a model building process or model generation process, using the data from the known samples corresponding to biological states such as described above. Such a dataset may be referred to as a “training dataset”.

A training dataset may comprise data from samples from two biological stages, for example, normal and diseased, benign and malignant, and early stage and late stage.

The classification model building process may comprise a number of steps. A dataset may be generated. The dataset may optionally be normalized. Thus, the concentrations of combinations of lipids from the known samples may be formed into a dataset. Other steps may be carried out on the concentrations, prior or subsequent to forming the dataset, for example, normalisation of the intensities or concentrations. Appendix D shows a dataset of normalised lipid concentrations from the lipids shown in Table D3, of normal and diseased (ovarian cancer), benign and malignant and early stage and late stage ovarian cancer samples used in the Examples.

The dataset may go through a first, analysis, step. The first analysis step may comprise Principal Components Analysis (PCA). The resulting dataset may go through a second, classification, step. The second classification step may comprise Support Vectors Machines (SVM) analysis. The classification model may be tested for performance. An iterative process may optionally be conducted to reduce the dimensions of the dataset. These are described in detail in the sections below.

Analysis Step

The first step in the classification model generation process may comprise a step we term an analysis step. This step may comprise an analysis of the dataset using a number of methods which result in a transformation of the dataset.

Methods suitable for use as a first analysis step include factor analysis and principal components analysis (PCA).

The analysis step may reduce the dimensions of the dataset. The analysis step may identify the principal components of the dataset. The analysis step may reduce the noise in the dataset. The analysis step may improve the signal-to-noise ratio. The analysis step may compress the data in the dataset.

The analysis step may result in a transformed dataset. The transformed dataset may have reduced dimensions as compared with the input dataset or it may result in a dataset with full dimensions (unreduced). Accordingly, although this first step may comprise a “dimension reduction” step, although it should be understood that the dimensions of the transformed dataset may not necessarily be reduced.

An output of the first step, for example, principal components analysis, may comprise a transformation matrix. Such a transformation matrix may be saved and form part of a classification model to be used for predictive analysis (described below).

Normalisation Step

Prior to the first analysis step of the classification model building process, an optional step of normalization may be performed.

Consider there are n samples in a training dataset and for each sample, m lipid intensities are measured. It is assumed that the total lipid intensity of each sample should be the same across all samples. Thus, the total lipid intensities of each sample are normalized to 1, as shown from Equation 1 below.

Let I_(ij) represent the intensity of i^(th) sample of lipid j and {right arrow over (x_(i))} represent a vector containing m normalized intensities of sample i.

$\begin{matrix} {{{\overset{\rightarrow}{x}}_{i} = {\begin{pmatrix} x_{i\; 1} \\ x_{i\; 2} \\ \vdots \\ x_{i\; m} \end{pmatrix} = {\frac{1}{\sum\limits_{j = 1}^{m}I_{i\; j}}\begin{pmatrix} I_{i\; 1} \\ I_{i\; 2} \\ \vdots \\ I_{i\; m} \end{pmatrix}}}},{{{where}\mspace{14mu} i} = 1},{\ldots \mspace{14mu} n}} & (1) \end{matrix}$

The first analysis step, for example, Principal Components Analysis, may then be performed on the normalized dataset X=({right arrow over (x)}₁, {right arrow over (x)}₂, . . . , {right arrow over (x)}_(n)).

The data is normalized by the total intensities in that mode and the concentrations calculated using the respective spiked standards.

The normalization could be given as:

${Lipid}_{i} = \frac{x_{i}}{\lbrack{Std}\rbrack \cdot {\sum\limits_{i = 1}^{n}x_{i}}}$

where x_(i) indicates the intensity of a particular type of lipid, lipid_(i). Std indicates the constant obtained from the standard graph or from a spiked lipid standard. In the case of the spiked standard, the Std is obtained as a ratio of the intensity to the amount (pmoles) of the standard spiked. Another way of obtaining Std is to plot a standard graph with varying amount of the standards and then obtaining a standard graph. This graph may then give the ratio of the intensity over concentration and can be substituted in the above equation.

Suitable lipids for spiking and determining concentrations are known in the art and are described in the Examples.

Principal Components Analysis (PCA)

The analysis step of the classification model generation process may comprise a step of Principal Components Analysis (PCA).

Principal Components Analysis is a technique to reduce multidimensional dataset C=({right arrow over (c)}₁, {right arrow over (c)}₂, . . . , {right arrow over (c)}_(m)) to a lower dimension.

Principal Components Analysis linearly transforms the original dataset into a new dimension space. This can be achieved using functions from a number of mathematical packages. For example, principal components analysis may be performed by Pirouette (Infometrix, Inc.), Statistica (StatSoft, Inc.), SPSS (SPSS Inc.), Unscrambler (CAMO Software AS.), PCA/X 5.0 (Windale Technologies Pty Ltd), XLSTAT (Addinsoft), StatistiXL (StatistiXL), NMath Stats (CenterSpace Software), R (Freeware) (http://rss.acs.unt.edu/Rdoc/library/pcaMethods/html/00Index.html), SAS/INSIGHT (SAS Institute Inc.). As a further example, the princomp function in Matlab may be used.

princomp in Matlab returns the principal component coefficients and the representation of X in the principal component space Y=({right arrow over (y)}₁, {right arrow over (y)}₂, . . . , {right arrow over (y)}_(n)). Each vector, {right arrow over (c)}_(i), represents the i^(th) principal component axis with dimension m. The relationship between C and Y is shown in Equation 2.

$\begin{matrix} {{{Y = {C \cdot \left( {X - {{rep}\left( \overset{\_}{X} \right)}} \right)}},{where}}\begin{matrix} \begin{matrix} {{\overset{\_}{X} = \begin{pmatrix} {\overset{\_}{x}}_{1} & {\overset{\_}{x}}_{2} & \ldots & {\overset{\_}{x}}_{n} \end{pmatrix}},} \\ {{{\overset{\_}{x}}_{i} = {\frac{1}{m}{\sum\limits_{j = 1}^{m}x_{i\; j}}}},} \end{matrix} & {{{rep}\left( \overset{\_}{X} \right)} = \begin{pmatrix} {\overset{\_}{x}}_{1} & {\overset{\_}{x}}_{2} & \ldots & {\overset{\_}{x}}_{n} \\ {\overset{\_}{x}}_{n} & {\overset{\_}{x}}_{2} & \ldots & {\overset{\_}{x}}_{n} \\ \vdots & \vdots & \ddots & \vdots \\ {\overset{\_}{x}}_{n} & {\overset{\_}{x}}_{2} & \ldots & {\overset{\_}{x}}_{n} \end{pmatrix}} \end{matrix}} & (2) \end{matrix}$

Principal components analysis involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. This helps in data compression. The outcome of these analysis is a score matrix.

Classification Step

The transformed dataset resulting from the first step may be go through a second step. The second step in the classification model building process may therefore comprise a classification step.

This step may comprise an analysis of the transformed dataset using a number of methods, such as support vector machines (SVM) analysis. The classification step may enable adequate classification of the samples, or it may be subject to further training steps (described below).

The classification analysis may result in an indicator of the class of the sample. The indicator of class may correspond to one of the biological states used for building the classification model. The correspondence between the biological states and the output of the second step may depend on the labels applied to the samples used as basis for the model generation. For example, in an SVM analysis, the labels may be either −1 or +1. Thus, −1 may represent a diseased state and +1 may represent a control state (i.e., a normal sample). As another example, −1 may represent a malignant state and +1 may represent a benign state. As a further example, −1 may represent a late stage state and +1 may represent an early stage state.

In such a case, where SVM analysis is applied, the outputs from the second step may be negative or positive. A negative output would indicate a “worse” condition, that is to say, a diseased (cancerous) sample, a malignant sample, or a late stage sample, as the case may be. A positive output, on the other hand, would in the situation above, indicate a “better” condition. That is to say, a positive output would indicate a normal sample, a benign sample or an early stage sample.

An output of the classification step may comprise a model, such as an SVM model where support vector machines (SVM) analysis is carried out as the second step. Such an SVM model may be saved and form part of a classification model to be used for predictive analysis (described below).

The outcome of the second classification step may be represented in the form of diagnostic utility matrix. The performance of the second classification step may be assessed using a number of factors, such as sensitivity, specificity and accuracy.

Support Vector Machines (SVM)

Support Vector Machines (SVM) is a supervised machine learning method used for classification and regression. SVM is described in detail in Review: Applications of Support Vector Machines in Chemistry, Rev. Comput. Chem. 2007, 23, 291-400.

SVM performs binary classification by constructing an l-dimensional hyperspace that optimally separates the dataset into two categories. The output value from the SVM is a real number which signifies its distance from the hyperplane. The SVM model may be saved (examples are shown in the Appendices).

SVM may be implemented using a number of packages, including SVMlight (svmlight.joachims.org/); SVMstruct (svmlight.joachims.org/svm_struct.html); mySVM (www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM/index.html); JmySVM (www-ai.cs.uni-dortmund.de/SOFTWARE/YALE/index.html); mySVM/db (www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVMDB/index.html); LIBSVM (www.csie.ntu.edu.tw/˜cjlin/libsvm/); looms (www.csie.ntu.edu.tw/˜cjlin/looms/); BSVM (www.csie.ntu.edu.tw/˜cjlin/bsvm/); SVMTorch (www.idiap.ch/learning/SVMTorch.html); Weka (www.cs.waikato.ac.nz/ml/weka/); SVM in R (cran.r-project.org/src/contrib/Descriptions/e1071.html); M-SVM (www.loria.fr/˜guermeur/); Gist (microarray.cpmc.columbia.edu/gist/); MATLAB SVM Toolbox (www.isis.ecs.soton.ac.uk/resources/svminfo/); TinySVM (chasen.org/˜taku/software/TinySVM/); SmartLab (www.smartlab.dibe.unige.it/); Gini-SVM (bach.ece.jhu.edu/svm/ginisvm/); GPDT (dm.unife.it/gpdt/); HeroSvm (www.cenparmi.concordia.ca/˜people/jdong/HeroSvm.html); Spider (www.kyb.tuebingen.mpg.de/bs/people/spider/); Java applets (svm.dcs.rhbnc.ac.uk/); LEARNSC (www.support-vector.ws/html/downloads.html); Tree Kernels (ai-nlp.info.uniroma2.it/moschitti/Tree-Kernel.htm); LS-SVMlab (www.esat.kuleuven.ac.be/sista/lssvmlab/); MATLAB SVM Toolbox (www.igi.tugraz.at/aschwaig/software.html); SVM/LOO (bach.ece.jhu.edu/pub/gert/svm/incremental/); SVMsequel (www.isi.edu/˜hdaume/SVMsequel/); LSVM (www.cs.wisc.edu/dmi/lsvm/); ASVM (www.cs.wisc.edu/dmi/asvm/); PSVM (www.cs.wisc.edu/dmi/svm/psvm/); OSU SVM Classifier Matlab Toolbox (www.ece.osu.edu/˜maj/osu_svm/); SimpleSVM Toolbox (asi.insa-rouen.fr/˜gloosli/simpleSVM.html); SVM Toolbox (asi.insa-rouen.fr/%7Earakotom/toolbox/index); MATLAB SVM Toolbox (theoval.sys.uea.ac.uk/˜gcc/svm/toolbox/); R-SVM (www.biostat.harvard.edu/˜xzhang/R-SVM/R-SVM.html); jSVM (www-cad.eecs.berkeley.edu/˜hwawen/research/projects/jsvm/doc/manual/index.html); SvmFu (five-percent-nation.mit.edu/SvmFu/); PyML (pyml.sourceforge.net/) and BioJava (www.biojava.org/).

SVM_(light) is a an open source implementation of SVM which may be obtained from http://svmlight.joachims.org/. It is one of the most widely used SVM classification and regression packages. It has a fast optimization algorithm, can be applied to very large datasets, and has a very efficient implementation of the leave-one-out cross-validation. It is distributed as C++ source and binaries for Linux, Windows, Cygwin, and Solaris. Kernels: polynomial, radial basis function, and neural (tanh).

SVM allows users to build in their own mathematical functions, which are known as kernel functions. In SVM_(light), the default kernel function is linear and it has also built in polynomial function, radial basis function (RBF), and sigmoid function (refer to Equation 3 below).

$\begin{matrix} {\varphi = \left\{ \begin{matrix} {x_{i}*x_{i}} & {Linear} \\ \left( {{\gamma \; x_{i}x_{j}} + {coefficient}} \right)^{degree} & {Polynomial} \\ {\exp \left( {{- \gamma}{{x_{i} - x_{j}}}^{2}} \right)} & {RBF} \\ {\tanh \left( {{\gamma \; x_{i}x_{j}} + {coefficient}} \right)} & {Sigmoid} \end{matrix} \right.} & (3) \end{matrix}$

Any of the kernel functions of SVM may be used for the SVM analysis described here. For example, the default kernel function of SVM_(light) may be implemented.

The SVM analysis may be conducted on a transformed dataset, that is to say, a dataset transformed by applying a transformation matrix to a dataset comprising the concentrations of a plurality of lipids.

Classification Model

A classification model is generated from the classification model generation process described above. The classification model may comprise a number of components.

The classification model may comprise a transformation matrix, also referred to as C, which is the obtained from the PCA analysis. The classification model may further comprise an SVM model, also referred to as S, which is the SVM model obtained from Y_(l). The classification model may yet further comprise other components, as described below.

The transformation matrix may comprise a full, un-reduced transformation matrix. Such a matrix may comprise an n×m transformation matrix, where n=number of lipids and m=number of principal components. In such a transformation matrix, n may be equal to m. Such a transformation matrix may comprise the total number of principal components.

The transformation matrix may comprise a reduced transformation matrix, in other words, a transformation matrix corresponding to a reduced number of principal components. For example, a reduced transformation matrix may comprise a n×m transformation matrix, where n=number of lipids and m=number of principal components retained (m<number of lipids). Such a reduced transformation matrix may be easily generated by forming a matrix comprising the rows of the full transformation matrix and the first m columns of the full transformation matrix.

The classification model generated may be evaluated by a number of factors such as receiver operating curve, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and accuracy. The performance of the model model may therefore be shown in terms of a number of factors, including for example the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, true negatives (TN), false negatives (FN), false positives (FP) and true positives (TP).

The model may further be evaluated using the N-fold cross validation and predicting the blinded samples.

Classification Model Training

As an optional step of the model generating process, a training process may take place as part of the model generation process.

The classification model may for example be adjusted and processed with an airh of establishing a model which achieve a certain predetermined level of performance.

The classification model may for example be adjusted and processed with an aim of establishing the minimum number of principal components which allow the model to achieve a certain predetermined level of performance.

The training may be conducted by assessing the output of the analysis step and selecting a number of factors to interpret.

Thus, the training process may comprise retaining principal components whose eigenvalues are greater than or equal to 1. This is also known as the Kaiser stopping rule (Kaiser, 1960).

The training process may comprise retaining principal components that explain a predetermined level of the variance in the dataset. The predetermined level of variance may comprise at least 55%, 65%, 75%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8% or 99.9%.

The training process alternatively, or in addition, comprise a scree test, as described by Cattell (1966). The magnitude of the eigenvalues on the vertical axis may be plotted against their number (e.g., first factor, second factor, third factor, etc). The training process may comprise retaining all factors in the sharp descent before the first eigenvector where the graph levels off, as described in Stevens, 1966.

A scree plot which shows principal components and eigenvalues may be used. The training process may comprise retaining principal components that in a scree plot of eigenvalues show a smooth decrease of eigenvalues. It may comprise retaining principal components that in a scree plot of eigenvalues are to the left of a levelling off in gradient in the plot. It may comprise retaining principal components that in a scree plot of eigenvalues are to the left of significant decrease in gradient in the plot. It may comprise retaining principal components that in a scree plot of eigenvalues are to the left of an elbow in the plot.

The training process may be conducted by assessing the output of the classification step.

Thus, the training process may comprise retaining principal components that perform to a predetermined level compared to the full dataset, as assessed by the performance of the classification model generated. For example, principal components may be retained to achieve at least 55%, 65%, 75%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9% or 100% of the performance of the classification model built using the full dataset. The performance of the classification model may be assessed by any combination of sensitivity, specificity, PPV, NPV, accuracy, true negatives (TN), false negatives (FN), false positives (FP) and true positives (TP).

The training process may comprise removing factors which do not significantly affect the performance of the SVM model, as assessed by any of the factors set out above.

The training process may take the form of an iterative process. Thus, the last (least significant) principal component determined by principal components analysis may be removed and the classification model built again. The new classification model may be compared to the original model for its classification performance. The performance may be assessed using a number of factors, such as sensitivity, specificity and accuracy. This process is then repeated until the predetermined level of performance is reached. For example, the process may be repeated until the model loses its performance.

In more detail, the first/row vectors of the transformed dataset Y may be selected to form an input vector, Y_(l). The input vector Y_(l) may then be subject to Support Vectors Machines (SVM) analysis. Y_(l) is equivalent to the input dataset in the principal component space, with reduced dimension of l. The value of l is the minimum dimension of the principal component space needed to obtain optimal classification in the SVM model. This may be established by iterative reduction of dimensions and testing the performance of the resulting model.

The level of performance may be any suitable level which meets the needs of the situation in question.

For example, an iterative reduction in dimensions may be carried out to train the classification model to achieve a performance which is at least as good as, or better than, a known biomarker. The biomarker may comprise, for example, CA-125. The sensitivity of a CA-125 is about 50%, the specificity about 97.15%, the PPV about 17.24% and the NPV about 99.39%.

Thus, the training may comprise determining the number of principal components needed to allow the classification model to achieve any one or more of a sensitivity of about 50% or more, a specificity about 97.15% or more, a PPV about 17.24% or more and an NPV about 99.39% or more.

Where classification model training takes place, the classification model may optionally further comprise a dimension, l. The dimension l may be a reduced dimension. The dimension l may comprise the dimension of principal component space, l, needed for optimal SVM classification.

The dimension l may be derived from principal components analysis of the dataset. This may be achieved by iterative reduction in dimensions and assessing performance of the resulting model (described above).

Classification Models Generated from 340 Lipids

Dataset of 340 Lipids

A dataset comprising concentrations of the 340 lipids shown in Table D3 may be obtained. The dataset may optionally be normalised.

Transformation Matrices (340 Lipids, Unreduced)

Principal Components Analysis may be conducted on the above dataset without dimension reduction.

We disclose in Appendix B1 a 340×340 transformation matrix of a classification model for classifying normal and diseased samples. We disclose in Appendix B2 a 340×340 transformation matrix of a classification model for classifying benign and malignant samples. We disclose in Appendix B3 a 340×340 transformation matrix of a classification model for classifying early and late stage samples.

SVM Models (340 Lipids, Unreduced)

The transformation matrices are used to transform the datasets of the respective sample pairs. SVM analysis is carried out on the transformed datasets.

As an example, SVM analysis may be conducted on a dataset of the concentrations the 340 lipids shown in Table D3 of normal and diseased (ovarian cancer) samples, transformed with a transformation matrix shown in Appendix B1. We disclose in Appendix C1 a resulting SVM model, which is also referred to in this document as “SVM-1”. Such an SVM model may be used, together with a 340×340 transformation matrix shown in Appendix B1, as part of a classification model for classifying normal and diseased (ovarian cancer) samples.

As another example, the SVM analysis may be conducted on a transformed dataset of the concentrations of the 340 lipids shown in Table D3 of benign ovarian cancer and malignant ovarian cancer samples, transformed with a transformation matrix shown in Appendix B2. We disclose in Appendix C2 a resulting SVM model, which is also referred to in this document as “SVM-2”. Such an SVM model may be used, together with a 340×340 transformation matrix shown in Appendix B2, as a classification model for classifying benign and malignant ovarian cancer samples.

As a further example, the SVM analysis may be conducted on a transformed dataset of the concentrations of the 340 lipids shown in Table D3 of early stage ovarian cancer and late stage ovarian cancer samples, transformed with a transformation matrix shown in Appendix B3. We disclose Appendix C3 a resulting SVM model, which is also referred to in this document as “SVM-3”. Such an SVM model may be used, together with a 340×340 transformation matrix shown in Appendix B3, as a classification model for classifying early and late ovarian stage cancer samples.

Classification Models (340 Lipids, Unreduced)

We disclose a classification model comprising a 340×340 transformation matrix shown in Appendix B1 and an SVM model shown in Appendix C1. Such a model may be used to distinguish between normal and diseased, for example, ovarian cancer, samples.

We disclose a further classification model which comprises a 340×340 transformation matrix shown in Appendix B2 and an SVM model shown in Appendix C2. Such a model may be used to distinguishing between benign and malignant samples.

We disclose yet another classification model which comprises a 340×340 transformation matrix shown in Appendix B3 and an SVM model shown in Appendix C3. Such a model may be used to distinguish between early and late stage samples.

Classification Models Generated from 340 Lipids (R1)

The training process may be conducted on a dataset comprising the concentrations of the 340 lipids shown in Table D3.

A further optional step of training the model by iterative reduction of dimensions may be carried out to determine the principal components needed for maximal classifying performance.

The cumulative performance of each of the classification models, as assessed by sensitivity, specificity, PPV and NPV, is shown in Appendices E1, E2 and E3.

We disclose in Appendix E1 the cumulative performance of a classification model (340 lipids) for normal versus diseased, by the number of principal components. We disclose in Appendix E2 the cumulative performance of a classification model (340 lipids) for benign versus malignant, by the number of principal components. We disclose in Appendix E3 the cumulative performance of a classification model (340 lipids) for early versus late, by the number of principal components.

The model with reduced dimensions may be tested for performance, such as by determining sensitivity, specificity, PPV, NPV, etc.

In these Appendices, rows in italics show the number of principal components required for a classification model which has identical performance compared to SVM analysis using the all the principal components.

In the case of the classification model for normal and diseased, maximal classifying performance (i.e., identical to a model with all the principal components) may be obtained using 85 principal components. In the case of the classification model for benign and malignant, maximal classifying performance may be obtained using 87 principal components. In the case of the classification model for early and late, maximal classifying performance may be obtained using 44 principal components.

Transformation Matrices (340 Lipids, Reduced)

We disclose a 340×85 transformation matrix comprising the first 85 columns of a matrix shown in Appendix B1, which may be used in a classification model for classifying normal and diseased, such as ovarian cancer, samples.

We disclose a 340×87 transformation matrix comprising the first 87 columns of a matrix shown in Appendix B2, which may be used in a classification model for classifying benign and malignant samples.

We disclose a 340×44 transformation matrix comprising the first 44 columns of a matrix shown in Appendix B3, which may be used in a classification model for classifying early and late stage samples.

SVM Models (340 Lipids, Reduced)

We disclose at Appendix C4 an SVM model which may be used with a 340×85 transformation matrix comprising the first 85 columns of a matrix shown in Appendix B1 as a classification model for classifying normal and diseased, such as ovarian cancer, samples.

We disclose at Appendix C5 an SVM model which may be used with a 340×87 transformation matrix comprising the first 87 columns of a matrix shown in Appendix B2 as a classification model for classifying benign and malignant samples.

We disclose at Appendix C6 an SVM model which may be used with a 340×44 transformation matrix comprising the first 44 columns of a matrix shown in Appendix B3 as a classification model for classifying early and late stage samples.

Classification Models (340 Lipids, Reduced)

We disclose a classification model which comprises a 340×85 transformation matrix comprising the first 85 columns of a matrix shown in Appendix B1 and an SVM model shown in Appendix C4. Such a model may be used to distinguish between normal and diseased, for example, ovarian cancer, samples.

We disclose a further classification model which comprises a 340×87 transformation matrix comprising the first 87 columns of a matrix shown in Appendix B2 and an SVM model shown in Appendix C5. Such a model may be used to distinguishing between benign and malignant samples.

We disclose yet another classification model which comprises a 340×44 transformation matrix comprising the first 44 columns of a matrix shown in Appendix B3 and an SVM model shown in Appendix C6. Such a model may be used to distinguish between early and late stage samples.

Classification Models Generated from 340 Lipids (R2)

The training process may be conducted on a dataset comprising the concentrations of the 340 lipids shown in Table D3.

A further optional step of training the model by iterative reduction of dimensions may be carried out to identify the number of principal components required for a classification model which provides performance which is as good as or better than CA-125.

The cumulative performance of each of the classification models, as assessed by sensitivity, specificity, PPV and NPV, is shown in Appendices E1, E2 and E3.

We disclose in Appendix E1 the cumulative performance of a classification model (340 lipids) for normal versus diseased, by the number of principal components. We disclose in Appendix E2 the cumulative performance of a classification model (340 lipids) for benign versus malignant, by the number of principal components. We disclose in Appendix E3 the cumulative performance of a classification model (340 lipids) for early versus late, by the number of principal components.

The model with reduced dimensions may be tested for performance, such as by determining sensitivity, specificity, PPV, NPV, etc.

In these Appendices, rows in bold show the number of principal components required for a classification model which provides performance which is as good as or better compared to CA-125 (Sensitivity=50%; Specificity=97.15%; PPV=17.24%; NPV=99.39%).

In the case of the classification model for normal and diseased, a classification model employing 10 principal components performs as well as or better than CA-125. In the case of the classification model for benign and malignant, a classification model employing 29 principal components performs as well as or better than CA-125. In the case of the classification model for early and late, a classification model employing 9 principal components performs as well as or better than CA-125.

Transformation Matrices (340 Lipids, Reduced)

We disclose a 340×10 transformation matrix comprising the first 10 columns of a matrix shown in Appendix B1, which may be used in a classification model for classifying normal and diseased, such as ovarian cancer, samples.

We disclose a 340×29 transformation matrix comprising the first 29 columns of a matrix shown in Appendix B2, which may be used in a classification model for classifying benign and malignant samples.

We disclose a 340×9 transformation matrix comprising the first 9 columns of a matrix shown in Appendix B3, which may be used in a classification model for classifying early and late stage samples.

SVM Models (340 Lipids, Reduced)

We disclose at Appendix C7 an SVM model which may be used with a 340×10 transformation matrix comprising the first 10 columns of a matrix shown in Appendix B1 as a classification model for classifying normal and diseased samples.

We disclose at Appendix C8 an SVM model which may be used with a 340×29 transformation matrix comprising the first 29 columns of a matrix shown in Appendix B2 as a classification model for classifying benign and malignant samples.

We disclose at Appendix C9 an SVM model which may be used with a 340×9 transformation matrix comprising the first 9 columns of a matrix shown in Appendix B3 as a classification model for classifying early and late samples.

Classification Models (340 Lipids, Reduced)

We disclose a classification model which comprises a 340×10 transformation matrix comprising the first 10 columns of a matrix shown in Appendix B1 and an SVM model shown in Appendix C7. Such a model may be used to distinguish between normal and diseased, for example, ovarian cancer, samples.

We disclose a further classification model which comprises a 340×29 transformation matrix comprising the first 29 columns of a matrix shown in Appendix B2 and an SVM model shown in Appendix C8. Such a model may be used to distinguish between benign and malignant samples.

We disclose yet another classification model which comprises a 340×9 transformation matrix comprising the first 9 columns of a matrix shown in Appendix B3 and an SVM model shown in Appendix C9. Such a model may be used to distinguish between early and late stage samples.

Classification Models Generated from N Lipids (N<340)

Classification may be carried out by generating classification models from subsets of the 340 lipids shown in Table D3 and application such classification models to unknown samples.

Datasets

Datasets comprising concentrations of any number of the 340 lipids shown in Table D3 may be derived. Such datasets may comprise concentrations of the first n lipids, where n<340, of Table D3.

Thus, for example, datasets comprising the concentrations of the first 2, 3, 4, 5, 6, 7, 8, 9, 10 lipids of Table D3 may be generated.

As another example, datasets comprising the concentrations of the first 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 or 50 lipids of Table D3 may be generated.

As another example, datasets comprising the concentrations of the first 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149 or 150 lipids of Table D3 may be generated.

As a further example, datasets comprising the concentrations of the first 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249 or 250 lipids of Table D3 may be generated.

As a yet further example, datasets comprising the concentrations of the first 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338 or 339 lipids of Table D3 may be generated.

Such datasets may be generated by, for example, selecting appropriate data from the Ovarian Cancer Training Dataset shown in Appendix D. The dataset may optionally be normalised.

Transformation Matrices (n Lipids)

Principal Components Analysis may be conducted on the above datasets with or without dimension reduction.

“Unreduced” n×n transformation matrices of respective classification models for classifying normal and diseased samples, benign and malignant samples and early and late stage samples may be generated by the methods described in this document, such as PCA.

“Reduced” n×m, where m<n, transformation matrices, representing a reduced number of principal components, may also be generated by, for example, iterative reduction of dimensions and testing for the performance of the resulting models, as described elsewhere in this document.

SVM Models (n Lipids)

The transformation matrices may be used to transform the datasets of the respective sample pairs. SVM analysis may be carried out on the transformed datasets.

As an example, SVM analysis may be conducted on a dataset of the concentrations of the n lipids of known normal and diseased (ovarian cancer) samples, transformed with a respective n×n “unreduced” transformation matrix described above. The resulting SVM model may be used, together with the n×n transformation matrix, as part of a classification model for classifying normal and diseased (ovarian cancer) samples.

As another example, the SVM analysis may be conducted on a dataset of the concentrations of the n lipids of known benign ovarian cancer and malignant ovarian cancer samples, transformed with a respective n×n “unreduced” transformation matrix described above. The resulting SVM model may be used, together with the n×n transformation matrix, as a classification model for classifying benign and malignant ovarian cancer samples.

As a further example, the SVM analysis may be conducted on a dataset of the concentrations of the n lipids of known early stage ovarian cancer and late stage ovarian cancer samples, transformed with a respective n×n “unreduced” transformation matrix described above. The resulting SVM model may be used, together with the n×n transformation matrix, as a classification model for classifying early and late ovarian stage cancer samples.

Corresponding SVM models generated on datasets transformed with n×m, where m<n, “reduced” transformation matrices may also be generated by the methods described in this document. Such SVM models together with respective “reduced” transformation matrices, as classification models for classifying normal and diseased samples, benign and malignant samples and early and late stage samples.

Classification Models (n Lipids)

We disclose a classification model comprising a n×n “unreduced” transformation matrix generated from a dataset of known normal and diseased (ovarian cancer) samples described above and a corresponding SVM model as described above. Such a model may be used to distinguish between normal and diseased, for example, ovarian cancer, samples.

We disclose a further classification model which comprises a n×n “unreduced” transformation matrix generated from a dataset of known benign and malignant samples described above and a corresponding SVM model as described above. Such a model may be used to distinguishing between benign and malignant samples.

We disclose yet another classification model which comprises a n×n “unreduced” transformation matrix generated from a dataset of known early stage and late stage samples described above and a corresponding SVM model as described above. Such a model may be used to distinguish between early and late stage samples.

We also disclose corresponding classification models comprising n×m, where m<n, “reduced” transformation matrices generated from datasets of known normal and diseased (ovarian cancer) samples, benign and malignant samples or early stage and late stage (ovarian cancer) samples and their corresponding SVM models, capable of classifying normal and diseased samples, benign and malignant samples and early and late stage samples respectively.

Classification Models Generated from 82 Lipids

Dataset of 82 Lipids

A dataset comprising concentrations of the 82 lipids shown in Table D2 may be obtained. The dataset may optionally be normalised.

Transformation Matrices (82 Lipids, Unreduced)

Principal Components Analysis may be conducted on the above dataset without dimension reduction.

We disclose in Appendix B4 a 82×82 transformation matrix of a classification model for classifying normal and diseased samples. We disclose in Appendix B5 a 82×82 transformation matrix of a classification model for classifying benign and malignant samples. We disclose in Appendix B6 a 82×82 transformation matrix of a classification model for classifying early and late stage samples.

SVM Models (82 Lipids, Unreduced)

The transformation matrices are used to transform the datasets of the respective sample pairs. SVM analysis is carried out on the transformed datasets.

As an example, SVM analysis may be conducted on a dataset of the concentrations the 82 lipids shown in Table D2 of normal and diseased (ovarian cancer) samples, transformed with a transformation matrix shown in Appendix B4. We disclose in Appendix C10 a resulting SVM model. Such an SVM model may be used, together with a 82×82 transformation matrix shown in Appendix B4, as part of a classification model for classifying normal and diseased (ovarian cancer) samples.

As another example, the SVM analysis may be conducted on a transformed dataset of the concentrations of the 82 lipids shown in Table D2 of benign ovarian cancer and malignant ovarian cancer samples, transformed with a transformation matrix shown in Appendix B5. We disclose in Appendix C11 a resulting SVM model. Such an SVM model may be used, together with a 82×82 transformation matrix shown in Appendix B5, as a classification model for classifying benign and malignant ovarian cancer samples.

As a further example, the SVM analysis may be conducted on a transformed dataset of the concentrations of the 82 lipids shown in Table D2 of early stage ovarian cancer and late stage ovarian cancer samples, transformed with a transformation matrix shown in Appendix B6. We disclose Appendix C12 a resulting SVM model. Such an SVM model may be used, together with a 82×82 transformation matrix shown in Appendix B6, as a classification model for classifying early and late ovarian stage cancer samples.

Classification Models (82 Lipids, Unreduced)

We disclose a classification model comprising a 82×82 transformation matrix shown in Appendix B4 and an SVM model shown in Appendix C10. Such a model may be used to distinguish between normal and diseased, for example, ovarian cancer, samples.

We disclose a further classification model which comprises a 82×82 transformation matrix shown in Appendix B5 and an SVM model shown in Appendix C11. Such a model may be used to distinguishing between benign and malignant samples.

We disclose yet another classification model which comprises a 82×82 transformation matrix shown in Appendix B6 and an SVM model shown in Appendix C12. Such a model may be used to distinguish between early and late stage samples.

Classification Models Generated from 44 Lipids

Dataset of 44 Lipids

A dataset comprising concentrations of the first 44 of the 340 lipids shown in Table D3 (i.e., as shown in Table E6) may be derived by, for example, selecting appropriate data from the Ovarian Cancer Training Dataset shown in Appendix D. The dataset may optionally be normalised.

Transformation Matrices (44 Lipids)

Principal Components Analysis may be conducted on the above dataset with or without dimension reduction.

“Unreduced” 44×44 transformation matrices of respective classification models for classifying normal and diseased samples, benign and malignant samples and early and late stage samples may be generated by the methods described in this document, such as PCA.

Respective 44×m, where m<44, “reduced” transformation matrices, representing a reduced number of principal components, may also be generated by, for example, iterative reduction of dimensions and testing for the performance of the resulting models, as described elsewhere in this document.

SVM Models (44 Lipids)

The transformation matrices may be used to transform the datasets of the respective sample pairs. SVM analysis may be carried out on the transformed datasets.

As an example, SVM analysis may be conducted on a dataset of the concentrations of the 44 lipids shown in Table E6 of normal and diseased (ovarian cancer) samples, transformed with a respective 44×44 transformation matrix described above. The resulting SVM model may be used, together with the 44×44 transformation matrix, as part of a classification model for classifying normal and diseased (ovarian cancer) samples.

As another example, the SVM analysis may be conducted on a dataset of the concentrations of the 44 lipids shown in Table E6 of benign ovarian cancer and malignant ovarian cancer samples, transformed with a respective 44×44 transformation matrix described above. The resulting SVM model may be used, together with the 44×44 transformation matrix, as a classification model for classifying benign and malignant ovarian cancer samples.

As a further example, the SVM analysis may be conducted on a dataset of the concentrations of the 44 lipids shown in Table E6 of early stage ovarian cancer and late stage ovarian cancer samples, transformed with a respective 44×44 transformation matrix described above. The resulting SVM model may be used, together with the 44×44 transformation matrix, as a classification model for classifying early and late ovarian stage cancer samples.

Corresponding SVM models generated on datasets transformed with 44×m, where m<44, “reduced” transformation matrices may also be generated by the methods described in this document. Such SVM models together with respective “reduced” transformation matrices, as classification models for classifying normal and diseased samples, benign and malignant samples and early and late stage samples.

Classification Models (44 Lipids)

We disclose a classification model comprising a 44×44 transformation matrix described above and an SVM model as described above. Such a model may be used to distinguish between normal and diseased, for example, ovarian cancer, samples.

We disclose a further classification model which comprises a 44×44 transformation matrix described above and an SVM model as described above. Such a model may be used to distinguishing between benign and malignant samples.

We disclose yet another classification model which comprises a 44×44 transformation matrix described above and an SVM model as described above. Such a model may be used to distinguish between early and late stage samples.

We also disclose corresponding classification models comprising 44×m, where m<44, “reduced” transformation matrices and their corresponding SVM models, capable of classifying normal and diseased samples, benign and malignant samples and early and late stage samples.

Classifications Model Generated from 77 Choline Lipids

Dataset of 77 Choline Lipids

A dataset comprising concentrations of the 77 choline lipids shown in Table D1 may be obtained. The dataset may optionally be normalised.

Transformation Matrices (77 Choline Lipids, Unreduced)

Principal Components Analysis may be conducted on the above dataset without dimension reduction.

We disclose in Appendix B7 a 77×77 transformation matrix of a classification model for classifying normal and diseased samples. We disclose in Appendix B8 a 77×77 transformation matrix of a classification model for classifying benign and malignant samples. We disclose in Appendix B9 a 77×77 transformation matrix of a classification model for classifying early and late stage samples.

SVM Models (77 Choline Lipids, Unreduced)

The transformation matrices are used to transform the datasets of the respective sample pairs. SVM analysis is carried out on the transformed datasets.

As an example, SVM analysis may be conducted on a dataset of the concentrations the 77 choline lipids shown in Table D1 of normal and diseased (ovarian cancer) samples, transformed with a transformation matrix shown in Appendix B7. We disclose in Appendix C13 a resulting SVM model. Such an SVM model may be used, together with a 77×77 transformation matrix shown in Appendix B7, as part of a classification model for classifying normal and diseased (ovarian cancer) samples.

As another example, the SVM analysis may be conducted on a transformed dataset of the concentrations of the 77 choline lipids shown in Table D1 of benign ovarian cancer and malignant ovarian cancer samples, transformed with a transformation matrix shown in Appendix B8. We disclose in Appendix C14 a resulting SVM model. Such an SVM model may be used, together with a 77×77 transformation matrix shown in Appendix B8, as a classification model for classifying benign and malignant ovarian cancer samples.

As a further example, the SVM analysis may be conducted on a transformed dataset of the concentrations of the 77 choline lipids shown in Table D1 of early stage ovarian cancer and late stage ovarian cancer samples, transformed with a transformation matrix shown in Appendix B9. We disclose Appendix C15 a resulting SVM model. Such an SVM model may be used, together with a 77×77 transformation matrix shown in Appendix B9, as a classification model for classifying early and late ovarian stage cancer samples.

Classification Models (77 Choline Lipids, Unreduced)

We disclose a classification model comprising a 77×77 transformation matrix shown in Appendix B7 and an SVM model shown in Appendix C13. Such a model may be used to distinguish between normal and diseased, for example, ovarian cancer, samples.

We disclose a further classification model which comprises a 77×77 transformation matrix shown in Appendix B8 and an SVM model shown in Appendix C14. Such a model may be used to distinguishing between benign and malignant samples.

We disclose yet another classification model which comprises a 77×77 transformation matrix shown in Appendix B9 and an SVM model shown in Appendix C15. Such a model may be used to distinguish between early and late stage samples.

Prediction Using Classification Model

The prediction process may be implemented in a similar manner to that of the model generation process. The prediction process classifies an unknown sample (i.e., a sample whose state is unknown) into one of two states.

The prediction process generally involves obtaining the concentrations of a plurality of lipids from an unknown sample. The lipid concentrations may be formed into a dataset. The dataset may optionally be normalized. These steps may essentially be performed as described above for model generation.

An appropriate classification model is then applied to the dataset of lipid concentrations.

An appropriate classification model suitable for classifying normal and diseased samples, benign and malignant samples and early stage and late stage samples, as set out in the sections “Classification Models Generated from 340 Lipids”, “Classification Models Generated from 340 Lipids (R1)”, “Classification Models Generated from 340 Lipids (R2)”, “Classification Models Generated from n Lipids (n<340)”, “Classification Models Generated from 82 Choline Lipids”, “Classification Model Generated from 44 Choline Lipids” and “Classification Model Generated from 77 Choline Lipids” may be applied to the data.

As described above, each classification model may comprise a transformation matrix and an SVM model. The dataset may go through a first, transformation, step. In this step, the dataset of lipid concentrations is transformed by a transformation matrix of the classification model. The resulting dataset may go through a second, classification, step. In this step, the resulting dataset is analysed by Support Vectors Machines (SVM), using an SVM model of the classification model.

A positive output of the SVM analysis indicates a normal, benign or early stage sample, as the case may be. A negative output of the SVM analysis indicates a diseased (ovarian cancer), malignant or late stage sample, as the case may be.

A schematic of the prediction process is shown in FIG. 13, which shows the optional normalization and dimension reduction steps. The prediction process is similar in some respects to the model generation process, but differs for example in that the PCA process may be replaced by transformation using matrix C defined in the classification model.

A single prediction process may be applied, or a number of prediction processes may be applied. Where this is the case, the classification models may be applied sequentially. Thus, a predictive process may be applied using a classification model for classifying normal and diseased samples, to indicate whether a sample is a normal sample or a diseased (i.e., ovarian cancer) sample. Where the sample is shown to be a diseased, ovarian cancer, sample, a further predictive process applying a classification model for classifying benign and malignant samples, to indicate whether a sample is a benign sample or a malignant ovarian cancer sample. Finally, where the sample is shown to be a malignant sample, a classification model for classifying early stage and late stage samples may be applied to the data, to indicate whether a sample is an early stage sample or a late stage ovarian cancer sample.

It will be clear that the bioinformatics methods of applying the classification models may be mixed and matched with other methods of classification, for example, histological staging and CA-125 serum levels.

Accordingly, an ovarian cancer sample diagnosed or determined by other means, e.g., histologically or biochemically, may be used as the subject of classification using the classification models described above. For example, an known ovarian cancer sample may be classed as a benign or malignant sample using a classification model capable of distinguishing benign and malignant samples.

Such an ovarian cancer sample, where it is classed as a malignant sample, may be subject to further analysis using a classification model capable of distinguishing between an early stage and a late stage sample, as described above, to class the sample as an early stage sample or a late stage sample. Similarly, such a classification model may be applied to lipid concentration data obtained form an ovarian cancer sample known to be a malignant sample (e.g., by histology) to establish whether the malignant sample is an early stage cancer sample or a late stage cancer sample.

In more detail, in the prediction process, the input sample with m lipids measured may be normalized such that the total lipid content is 1. The normalized data may then be transformed into the principal component space defined in the classification model, which is characterized by the matrix C. Upon obtaining the output vector, the transformed vector may be fed into the SVM model, which is stored in the classification model. Optionally, the first l rows, where l is a parameter stored in the classification model, of the transformed vector are selected and fed into SVM. The SVM model then outputs a float value whereby a negative value represents a label of −1 (diseased; benign; early stage) and a positive value represents a label of +1 (normal; malignant; late stage).

EXAMPLE

The following describes an example of prediction using the methods and compositions described here. A blinded sample from the data obtained in the Examples, sample Id is 270, is used in this example.

The lipid concentrations from the sample are obtained. This data is passed through the prediction models based on the SVM obtained from the training set. When the data is passed through SVM-1 (Appendix C1, predicts if the subject is normal or has ovarian growth). A positive value should indicate that the subject is a control (i.e., undiseased) and a negative value should indicate that the subject has growth in the ovary.

The value obtained from the subject sample 270 using the above analysis −1.702055. Since this value is negative it indicates that the sample is a cancerous sample (i.e., the subject has an ovarian cyst/growth).

Accordingly, this data is then passed through SVM-2 (Appendix C2) which predicts if the growth is benign or malignant. Here the positive value should indicate a benign condition and a negative value should indicate malignancy.

The value obtained for the sample 270 is +1.104463. Since this is a positive value it is predicted as benign growth.

The sample is unblinded. The records show that the actual sample ID is 14757 (this ID is the actual ID used by a collaborator) and is a benign condition. The details of this blinded samples are as follows: Age 46 yrs, Ethnicity Chinese, Type of Cyst Benign, Subtype: Serous, CA125 test: 7.5 (the CA-125 test is a clinically used test and the value of 7.5 indicates that the sample is normal).

It will be appreciated that although the status of this blinded sample has been determined by other means, but is hidden, the exact same steps described below may be implemented on a sample of completely unknown status.

Treatment

Where samples and patients have been determined to have ovarian cancer, or are staged, etc, appropriate treatments may be given to them. For example, where appropriate, surgery to remove the cancerous tissue may be carried out. Radiotherapy or chemotherapy may also be applied.

Chemotherapy for localised ovarian cancer may comprise Carboplatin or a combination of Paclitaxel (Taxol) and carboplatin. The chemotherapy may for example be given intravenously. For recurrent ovarian cancer, carboplatin and paclitaxel (Taxol) treatment may be applied. Other treatments may include Paclitaxel (Taxol) alone, Topotecan or Liposomal doxorubicin (also called Caelyx or Doxil)—a type of a chemotherapy drug, doxorubicin. Other drugs may also be used, either alone or in combination.

If cancer comes recurs than 6 months after initial chemotherapy, Paclitaxel (Taxol) alone, Liposomal doxorubicin, Topotecan (Hycamtin), Gemcitabine or Cisplatin may be applied.

Examples

Using computational technologies, we have generated a diagnostic model to determine if a biological sample exhibits or is predictive or suggestive of a particular biological state. Such states may be associated with one or more diseases or physiological status.

To produce such a model, a number of samples having a known biological state are analyzed and compared with samples known to have been taken from patients who do not have that biological state. These data are then input into a modeling program, as described below, to find discriminatory patterns that are specific to a particular biological state. Such patterns are based upon various combinations of features or markers found in the data derived from the samples, such as lipid concentrations.

FIG. 1 is a chart illustrating the work flow for development of binary classifiers for ovarian cancers based on multiparameter analysis of plasma lipids and supervised learning. The flow diagram summarises the methodology for building the prediction model and diagnosis of unknown samples. FIG. 2 is a flowchart showing the training process.

Example 1 Sample Preparation and Analysis: Lipid Extraction

Lipids are extracted from 50 μl of blood samples using the modified Bligh Dyers extraction method.

Briefly, 600 μl of ice cold chloroform-methanol, 1:2 (v/v) is added to 50 μl of blood plasma along with the internal standards (Table E1) and vortexed vigorously for 1 min.

Table E1 Standards Lipid ug Species spiked 1 17:0 Lyso GPA 2.5 2 14:0 Lyso GPEtn 2.5 3 19:0 Cer 1 4 8:0 Glu Cer 0.5 5 12:0 SM 2.5 6 di 14:0 GPGro 0.5 7 di 20:4 GPA 2.5 8 di 8:0 GPIns 0.5 9 di 22:6 GPSer 2.5

The samples are incubated in ice for 10 min. followed by the addition of 300 chloroform. The phase is broken with the addition of 200 μl of 0.1 M HCl or water and vortexed vigorously for 2 min. The phases are separated by centrifugation and the lower organic phase containing the lipids is transferred to fresh tube.

The lipids are re-extracted from the aqueous phase with 300 μl of chloroform pooled with the first organic extract and dried under vacuum. The extracted lipids are stored at −80° C. before analysis. Great care and precaution needs to be taken to ensure the generation of consistent and matched extract libraries.

The lipids are suspended in 200 μl of chloroform: methanol 1:1 (v/v) and used for mass spectrometry analysis.

Example 2 Sample Preparation and Analysis: Lipid Analysis

The lipids are initially separated on Waters XTerra C18 reverse phase column (1 mm×150 mm) column before entering into the mass spectrometer.

Typically, 5 μl of sample is injected for analysis. The inlet system consisted of a Waters CapLC autosampler, and a Waters CapLC pump. Chloroform-methanol 1:1 (v/v) with 15 mM piperidine is used as the mobile phase for isocratic elution at a flow rate of 15 μl/min. The column elutes are measured using Electrospray ionization mass spectrometry (ESI-MS) through a Waters Micromass Q-T of micro mass spectrometer operated in the negative ion mode. The capillary voltage and sample cone voltage are maintained at 3.0 kV and 50 V, respectively. The source temperature is 80° C. and the nanoflow gas pressure is maintained at 0.7 bars.

The mass spectrum is acquired from m/z 400 to 1600 in the negative or positive ion mode with an acquisition time of 20 min; the scan duration is 1.2 s. A representative spectra obtained in negative mode for one of the embodiment is depicted in FIG. 3. Individual molecular species are identified using tandem mass spectrometry and the collision energy used ranged from 25 to 80 eV.

Further identification and characterization of the lipid species is carried out using a 4000 Q-Trap mass spectrometer. The presence of a particular class of lipid is identified by product ion, precursor ion and neutral loss scans.

For GPEtn, GPIns and GPCho, the precursor ion scan for m/z 196 (negative mode), 241 (negative mode) and 184 (positive mode) respectively is carried out. For GPSer, neutral loss of 87 is monitored. For sphingolipids, precursor scans of 264, 266 and 184 are used. Presence of 153 is used for detection of GPA, GPGro as well as other phospholipids. The lipid molecules are further characterized and confirmed by tandem mass spec (MSMS) scans along with the other species like GPA, GPGro, Cardiolipin, Sulfatides, Ceramides and Spingomyelins. The characteristics daughter ion for each lipid molecule is noted.

Example 3 Sample Preparation and Analysis: Lipid Quantification

Quantification of individual lipid molecular species is performed using MRM with an Applied Biosystems 4000 Q-Trap mass spectrometer.

Samples are directly introduced into the mass spectrometry using an Agilent autosampler. In these experiments, the first quadrupole, Q1, are set to pass the precursor lipid ion of interest to the collision cell, Q2, where it underwent collision-induced dissociation. The third quadruple, Q3, is set to pass the structure specific product ion characteristic of the precursor lipid of interest.

Each individual ion dissociation pathway for the lipid species is optimized with regard to collision energy (CE), collision cell exit potential (CXP) and declustering potential (DP) with a dwell time of 25 msec. This is to minimize variations in relative ion abundance due to differences in rates of dissociation. The ion source gas GS1 and GS2 is maintained at 20 and 50 p.s.i. respectively and the curtain gas at 20 p.s.i. The entrance potential (EP) is set at 10.

In order to prevent the loss of the sensitivity of the method, the list is divided into 3 independent analysis sets. The first set consisted of the lipid classes belonging to GPA (Table E2A, Appendix A), GPGro (Table E2B, Appendix A), GPSer (Table E2C, Appendix A), Sulfatides (Table E2D, Appendix A) and Cardiolipins (Table E2E, Appendix A) monitored in the negative mode.

The second set consisted of the GPEtn (Table E2F, Appendix A), GPIns (Table E2G, Appendix A) and phosphoinositide phosphates (Table E2H, Appendix A) in negative mode.

The third set monitored the ions in the positive mode consisting of GPCho (Table E21, Appendix A), Spingomyelins (Table E2J, Appendix A), Ceramides (Table E2K, Appendix A) and their glycosylated derivative (Table E2L and Table E2M, Appendix A).

An optimized 15 μl of samples is injected per run per set with chloroform-methanol 1:1 (v/v) as the mobile phase at the flow rate of 200 μl/min. The run is carried out for 2 min.

Example 4 Data Analysis and Bioinformatics: Concentration Determination—Protocol for Determining Absolute Concentrations of Lipids using Spiked Standards and Standard Curve

The data is normalized by the total intensities in that mode and the concentrations calculated using the respective spiked standards.

The normalization could be given as:

${Lipid}_{i} = \frac{x_{i}}{\lbrack{Std}\rbrack \cdot {\sum\limits_{i = 1}^{n}x_{i}}}$

x_(i) indicates the intensity of a particular type of lipid, lipid_(i). Std indicates the constant obtained from the standard graph or from the spiked lipid standard. In the case of the spiked standard the Std is obtained as a ratio of the intensity to the amount (pmoles) of the standard spiked. The other way is to plot a standard graph with varying amount of the standards and then obtaining a standard graph. This graph then could give the ratio of the intensity over concentration and can be substituted in the above equation.

Depending upon the type of the lipid for which the concentration is to be calculated the respective standard is taken. A list of spiked standards and the lipids the spiked standards can be used to quantify is given in the table below.

Lipid Spiked Can be used for quantification of 1 17:0 Lyso GPA Lyso GPA 2 14:0 Lyso GPEtn Lyso GPEtn, GPEtn 3 19:0 Cer Cer 4 8:0 Glu Cer MonoHexCer and DiHexCer 5 12:0 SM SM and Lyso GPCho, GPCho 6 Di 14:0 GPGro Lyso GPGro, GPGro, Cardiolipin 7 Di 20:0 GPA GPA 8 Di 8:0 GPIns GPIns, GPInsP, GPInsP2, Sulfatide 9 Di 22:6 GPSer Lyso GPSer, GPSer

Example 5 Data Analysis and Bioinformatics: Principal Components Analysis (PCA) and Support Vector Machines (SVM)

The data is then processed using multivariate analysis (Principal Components Analysis).

Principal components analysis may be performed by a number of packages, for example Pirouette (Infometrix, Inc.), Statistica (StatSoft, Inc.), SPSS (SPSS Inc.), Unscrambler (CAMO Software AS.), PCA/X 5.0 (Windale Technologies Pty Ltd), XLSTAT (Addinsoft), StatistiXL. (StatistiXL), NMath Stats (CenterSpace Software), R (Freeware) (http://rss.acs.unt.edu/Rdoc/library/pcaMethods/html/00Index.html), SAS/INSIGHT (SAS Institute Inc.).

The outcome of the Principal Components Analysis is a score matrix. The score matrices are used as inputs into supervised learning methods like Support Vector Machine (SVM) for classification based on their diseased conditions.

SVMs are supervised learning methods which can perform binary classification (Noble, 2006). They map input feature vectors to a higher dimensional feature space where a maximal separation hyperplane is constructed.

SVM is implemented using SVMLight, which is executed in C. SVM models are built using default linear kernel functions. The output value from the SVM is a real number which signifies its distance from the hyperplane.

The outcome of the SVM is represented in the form of diagnostic utility matrix and the performance of the model is based on sensitivity, specificity and accuracy.

The model may then go to an iterative reduction in the dimension. The last PC is removed and the model built again. The new model is compared to the original model for its performance. This is repeated till the model loses its performance.

The final model generated is then evaluated by receiver operating curve, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and accuracy.

The model is then evaluated using the N-fold cross validation and predicting the blinded samples.

Example 6 Data Analysis and Bioinformatics: Statistical Considerations

The model is evaluated using receiver operating characteristics (ROC) curve, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Sensitivity is the probability that a person who is positive are tested positive. Specificity is the probability that a person who is negative are tested negative. Sensitivity and specificity are also known as true positive and true negative rate respectively. Positive predictive value (PPV) gives the probability that a person tested positive is positive. Negative predictive value (NPV) gives the probability that a person tested negative is negative.

Other statistical tests like the student's t-test in case of normality in distribution or Mann Whitney U Test in case of non-normality in distribution, Kolmogorov-Smirnov goodness-of-fit hypothesis test are used to test for normality.

Example 7 Diagnostic Test for Ovarian Tumours

In this study, the MRM data is obtained to build a prediction model for predicting malignancy of ovarian cancer.

138 samples are used as a training set for model building. 383 lipids measurements are obtained for each and every sample. PCA is used to reduce the dimension of the training set and the reduced set is used as input feature vectors for SVM. Two SVM models are built using linear and quadratic kernel functions.

Example 8 Diagnostic Test for Ovarian Tumours: Patients and Procedures

All patient-derived biological specimens are collected under protocols approved by the National Health Group Institutional Review Board, Singapore and all participants provided written informed consent.

Whole blood samples are obtained preoperatively in EDTA tubes by routine venipuncture of women undergoing surgery for suspected ovarian cancer in National University Hospital (NUH), Singapore. All women ages 16 to 81 years undergoing surgery for suspected ovarian cancer are regarded as eligible for entry into the study. All of the samples are collected before the day of surgery or treatment. The plasma is immediately isolated and stored at −70° C. Just before the lipid extraction, the plasma is thawed. Of the preoperative samples obtained, 99 are from women who are later confirmed to have ovarian cancer.

Whole blood samples from control subjects are collected concurrently from healthy women from the same counties who reported no history of cancer, gynecologic disease, oophorectomy or family history of breast/ovarian cancer. The controls are either volunteers or are on routine checkup at the family care centers. All subjects recruited in this study are non-smokers. Whole blood specimens are obtained from a total of 99 ovarian cancer patients, including 40 patients with benign tumor, 22 patients at the early malignant stage (Stage I and II) and 37 at the late malignant stage (Stage III and IV).

Cancer diagnosis is confirmed for all cases by review of pathology records by a single ovarian cancer expert. Clinical stage is determined according to International Federation of Gynecologists and Obstetricians criteria, and the histological subtype is evaluated according to the WHO classification.

Using this methodology, 211 plasma samples are obtained which include the controls and patients are diagnosed as benign or with the malignant stages. The diagnosis is done using the Trans-vaginal Ultrasonography (TVU), Serum CA125 level and then confirmed by biopsies. The stage 1 and 2 of malignancy is classified as early malignant and the stage 3 and 4 as late malignant. The control samples are collected after careful examination of the donors.

Of these 211 samples, 138 samples are randomly selected irrespective of their pathological conditions. This formed a training set and the remaining 73 samples are blinded by the hospital staff and disclosed only after the complete analysis is done. The split up of the samples and their subsets is depicted in FIG. 4.

The training set included 39 plasma samples from control individuals and 99 from women with various forms of ovarian cancers. Out of the 99 patients samples 40 of the patients had tumors of benign characteristics and the remaining 59 are malignant tumors (22 of which are in the early stages and 37 in their late stages). The demographic profiling of these samples is represented in Table E3.

TABLE E3 Demographic Profiling of Plasma Samples in Training Set Malig- Control Patient Benign nant Early Late Total Age Ave Age NA 47 41 52 43 53 Max NA 81 74 81 74 81 Min NA 16 18 16 20 16 Median NA 48 41 53 48 56 Ethnicity Chinese 23 67 26 41 17 24 90 Malay 7 20 9 11 2 9 27 Indian 6 6 3 3 2 1 12 Other 3 6 2 4 1 3 9 Sum 39 99 40 59 22 37 138 Tumor type Endometroid 26 11 15 6 9 26 Mucinous 22 13 9 8 1 22 Ser/Pap 27 5 22 3 19 27 Dermoid 6 6 0 0 0 6 Physiol/foll 3 3 0 0 0 3 Clear Cell 8 0 8 5 3 8 Mixed Germ 1 0 1 0 1 1 other 6 2 4 0 4 6

For the ovarian cancer patients various types of tumors are taken into account. The type of tumor is classified by various pathological test conducted on the tumor tissue. Control and patient samples are arranged randomly before lipid extraction.

Example 9 Diagnostic Test for Ovarian Tumours: Sample Analysis

Samples are extracted as described in Example 1 above and analyzed on an Applied Biosystems 4000 Q-TRAP mass spectrometer with the MRM method described.

Data peaks are integrated and normalized with respect total lipid content and ion response of internal standards.

The lipid species are graphically represented as heat plot (FIG. 5).

Example 10 Diagnostic Test for Ovarian Tumours: Data Analysis and Bioinformatics

A broad look of the lipid profile doesn't suggest drastic differences in the lipid profile between the samples. Plasma levels within the eleven classes of lipids covered here were very comparable between the various cases and control populations with no apparent differences that would be obvious without thorough statistical evaluation.

Principal Components Analysis (PCA)

Principal Components Analysis is conducted on a dataset comprising the concentrations of the 340 lipids shown in Table D3.

The resulting score matrices (transformation matrices) are set out in the Appendices and are described below and in the detailed description above. The transformation matrices resulting from the PCA analysis may be used to form a classification model.

The principal components analysis (PCA) generated through orthogonal linear transformation of the dataset shows that the controls patients are indeed separated from the patient population (FIG. 6A) on a three dimensional projection (1^(st), 2^(nd) and 3′^(d) principal components).

The first 3 principal components are able to define ˜70% of the variance based on the Scree plot (FIG. 6B). The classifiers based on the PCA analysis are compiled and the list of these probable biomarker lipids are listed (Table E4).

TABLE E4 Lipid Classifiers for Principal Components Analysis No Q1 mass Q3 mass Lipid Species 1 758.7 184.1 GPCho:34:2a 2 703.8 184.4 Sphingomyelin:d18:1/16:0 3 786.6 184.1 GPCho:36:2a 4 784.6 184.1 GPCho:36:3a 5 813.9 184.4 Sphingomyelin:d18:1/24:1 6 782.6 184.1 GPCho:36:4a 7 759.8 184.4 Sphingomyelin:d18:1/20:0 8 760.6 184.1 GPCho:34:1a 9 810.6 184.1 GPCho:38:4a 10 704.6 184.1 GPCho:30:1a 11 731.8 184.4 Sphingomyelin:d18:1/18:0 12 814.6 184.1 GPCho:38:2a 13 496.4 184.1 GPCho:Lyso 16:0 14 808.6 184.1 GPCho:38:5a 15 815.6 184.1 Sphingomyelin:18/24:0 16 520.4 184.1 GPCho:Lyso 18:2 17 761.8 184.4 Sphingomyelin:d18:0/20:0 18 787.9 184.4 Sphingomyelin:d18:1/22:0 19 743.8 279.3 GPGro:18:2/16:1 20 812.6 184.1 GPCho:38:3a 21 524.4 184.1 GPCho:Lyso 18:0 22 732.6 184.1 GPCho:32:1a 23 762.6 184.1 GPCho:34:0a 24 788.6 184.1 GPCho:36:1a 25 756.6 184.1 GPCho:34:3a 26 544.4 184.1 GPCho:Lyso 20:4 27 834.6 184.1 GPCho:40:6a 28 568.4 184.1 GPCho:Lyso 22:6 29 745.8 281.3 GPGro:18:1/16:1 30 522.4 184.1 GPCho:Lyso 18:1

Analysis of these lipids showed that mostly the GPCho and the sphingomyelins showed high ability to discriminate between the controls and the patient samples.

Appendix B1 shows a 340×340 transformation matrix of a classification model for classifying normal and diseased samples.

Similar analysis is carried out to differentiate between benign/malignant (FIG. 6C and FIG. 6D) and early/late forms of cancer (FIG. 6E and FIG. 6F).

Appendix B2 shows a 340×340 transformation matrix of a classification model for classifying benign and malignant samples. Appendix B3 shows a 340×340 transformation matrix of a classification model for classifying early and late samples.

Interestingly, PCA is able to separate the benign from the malignant samples, but the late from early are not well clustered. The Scree plot showed that the first 3 components are able to define nearly 70% of the variance for the benign vs. malignant dataset.

The data is also analyzed to check whether it is able to differentiate the malignant samples from a combination of benign and controls (FIG. 6G and FIG. 6F).

Support Vector Machines (SVM) Analysis

To evaluate the robustness of the profiling of lipids, a SVM based approach for classification of the samples is used.

Support Vector Machines analysis is conducted on a transformed dataset of the concentrations the 340 lipids shown in Table D3. The resulting SVM models are set out in the Appendices and are described below and in the detailed description above. The SVM models resulting from the SVM analysis may be used to form a classification model.

SVM is implemented using SVMLight. The PCA score matrix (N=138 samples by L=360 PC in case of the full training set) is used as the input feature vector for a support vector machine.

The output from the SVM model is assessed in the form of sensitivity, specificity and accuracy and used to describe the diagnostic utility (FIG. 7). The output of each SVM step can be visualized in a 2×2 matrix displaying true and false positives and negatives.

Appendix C1 shows an SVM model of a classification model for classifying normal and diseased samples. Such an SVM model may be used with a 340×340 transformation matrix shown in Appendix B1 and described above in the PCA section of this Example.

Appendix C2 shows an SVM model of a classification model for classifying benign and malignant samples. Such an SVM model may be used with a 340×340 transformation matrix shown in Appendix B2 and described above in the PCA section of this Example.

Appendix C3 shows an SVM model of a classification model for classifying early and late samples. Such an SVM model may be used with a 340×340 transformation matrix shown in Appendix B3 and described above in the PCA section of this Example.

Training

We next trained the SVM by iterative reduction of dimensions until a minimal set of PCs was found that resulted in maximal classifying performance (SVM model). This step is optional and is not strictly required.

The maximal classifying performance could comprise identical or similar performance compared to SVM analysis using the all or substantially all of the principal components (i.e., un-reduced). Performance may be assessed by any one or more, preferably all, of sensitivity, specificity, PPV, NPV, accuracy, true negatives (TN), false negatives (FN), false positives (FP) and true positives (TP).

The training process was done using the dataset comprising the concentrations of the 340 lipids shown in Table D3. The SVM models resulting from the SVM analysis conducted above using the training sets are set out in the Appendices and are described below and in the detailed description above.

Note that SVMs are binary classifiers. Thus, the above process was repeated to build SVM models for classification of patient vs. control (Patient/Control), i.e., diseased vs Normal, malignant vs. benign (Malignant/Benign) forms of cancers. A sequential arrangement of such binary classifiers can then be used for classification of samples from selected populations.

The cumulative performance of each of the classification models, as assessed by sensitivity, specificity, PPV and NPV, is shown in Appendices E1, E2 and E3. Appendix E1 shows the cumulative performance of a classification model (340 lipids) for normal versus diseased, by the number of principal components. Appendix E2 shows the cumulative performance of a classification model (340 lipids) for benign versus malignant, by the number of principal components. Appendix E3 shows the cumulative performance of a classification model (340 lipids) for early versus late, by the number of principal components. Rows in italics show the number of principal components required for a classification model which has identical performance compared to SVM analysis using the all the principal components.

In the case of the classification model for normal and diseased, maximal classifying performance may be obtained using 85 principal components. Appendix C4 shows the resulting SVM model. Such an SVM model may be used with a 340×85 transformation matrix comprising the first 85 columns of a matrix shown in Appendix B1 for classifying normal and diseased samples.

In the case of the classification model for benign and malignant, maximal classifying performance may be obtained using 87 principal components. Appendix C5 shows the resulting SVM model. Such an SVM model may be used with a 340×87 transformation matrix comprising the first 87 columns of a matrix shown in Appendix B2 for classifying benign and malignant samples.

In the case of the classification model for early and late, maximal classifying performance may be obtained using 44 principal components. Appendix C6 shows the resulting SVM model. Such an SVM model may be used with a 340×44 transformation matrix comprising the first 44 columns of a matrix shown in Appendix B3 for classifying early and late samples.

Example 11 Diagnostic Test for Ovarian Tumours: Model Results

The model showed the ability to discriminate the following conditions.

Normal vs Diseased

98 of the 99 diseased samples are predicted as samples from cancer patients and 36 out of the 39 controls are diagnosed as normal (FIG. 7A).

Benign vs Malignant

The power of the diagnostic tool can be appreciated from its ability to distinguish the benign from the malignant samples.

Of the 59 malignant, only 3 of the samples showed a false negative (FIG. 7B). The sensitivity, specificity and accuracy of this model are 95% (56 of 59), 83% (33 of 40) and 90% (89 of 99) (Table E5).

Early vs Late

Interestingly, the model is also able to show a good degree of classification between the early and the late stages of malignancy with 100% sensitivity (37 of 37), 82% specificity (18 of 22) and 93% accuracy (55 of 59) (FIG. 7C).

Normal or Benign vs Malignant

In a clinical scenario the benign growth is not considered life threatening and hence the lipid profiling is checked if it could also classify between the normal and the benign patients against the more drastic malignant cases.

The model shows that it is able to distinguish the malignancy from the rest of the samples with just 5 of the malignant samples falling in the false positive region (FIG. 7D). Malignancy could be distinguished from the rest of the samples with a specificity and sensitivity of 88% and 92%, respectively.

Benign vs Early

Moreover, the lipid profiling is also able to differentiate between benign and the early stages of cancer (FIG. 7E).

TABLE E5 Diagnostic Utility Matrix Analysis Lipidomic Cont + CA125 Control Benign Early Benign Benign Vs Vs Vs Vs Vs Patient Malignant Late Malignant Malignant True 98 56 37 52 43 Positive (TP) True 36 33 18 73 31 Negative (TN) False 3 7 4 6 9 Positive (FP) False 1 3 0 7 15 Negative (FN) Total 138 99 59 138 98 Sensitivity 98.99 94.92 100.00 88.14 74.14 Specificity 92.31 82.50 81.82 92.41 77.50 PPV 97.03 88.89 90.24 89.66 82.69 NPV 97.30 91.67 100.00 91.25 67.39 Accuracy 97.10 89.90 93.22 90.58 75.51 Prevalance 71.74 59.60 62.71 42.75 59.18 False 7.69 17.50 18.18 7.59 22.50 positive rate False 1.01 5.08 0.00 11.86 25.86 Negative rate Odds Ratio 1176.00 88.00 90.38 9.87 Cohens Kappa 0.93 0.79 0.85 0.84 0.50

Example 12 Diagnostic Test for Ovarian Tumours: Comparison of Model with CA125 Test

To determine whether lipidomic based approach offers a diagnostic advantage over the traditional CA125 test, the accuracy of these tests are compared.

The CA125 test which is gathered from the patients samples are analysed for their diagnostic ability. The same sets of samples are tested for the levels of CA125. The clinically approved level of 35 units/ml is taken as a cut-off to separate the benign from the malignant.

The CA125 test for discriminating between benign and malignancy showed a sensitivity of 74% (43 of 58), specificity of 78% (31 of 40) and accuracy of 76% (74 of 98). The positive predictive value for both the models are comparable but the negative predictive values showed superior performance of 92% when lipidomic based SVM is used as compared to just 59% for CA125. These values for CA125 are better than some of the reported diagnostic utility values for CA125.

Comparison between the models for various statistical diagnostic utility parameters based on SVM is summarized in Table E5. The lipid profiling based model showed superior prediction to differentiate between benign and malignant in comparison to the CA125 test. The model for the CA125 is moderate as the Cohen's Kappa value is only 0.5 as compared to 0.79 for the lipid profile based model which is considered substantial.

The receiver operating characteristics (ROC) curves for the models are plotted and all the models performed better (FIG. 8). The method for diagnosing the difference between benign and malignant also confirmed the superiority of the model over the CA125 test (FIG. 9).

Example 13 Diagnostic Test for Ovarian Tumours: Training and Deriving Minimal Principal Components

We further trained the SVM by iterative reduction of dimensions to determine the minimal number of principal components that would provide performance which is as good as or better compared to a CA-125 (Sensitivity=50%; Specificity=97.15%; PPV=17.24%; NPV=99.39%). For example, the number of principal components is chosen such that the model has a sensitivity and specificity which is higher than CA-125.

As described above in Example 10 (under Training), the cumulative performance of each of the classification models is set out in Appendices E1, E2 and E3. Rows in bold show the number of principal components required for a classification model which performs better than CA-125.

In the case of the classification model for normal and diseased, a classification model employing 10 principal components performs as well as or better than CA-125. Appendix C7 shows the resulting SVM model. Such an SVM model may be used with a 340×10 transformation matrix comprising the first 10 columns of a matrix shown in Appendix B1 for classifying normal and diseased samples.

In the case of the classification model for benign and malignant, maximal classifying performance may be obtained using 29 principal components. Appendix C8 shows the resulting SVM model. Such an SVM model may be used with a 340×29 transformation matrix comprising the first 29 columns of a matrix shown in Appendix B2 for classifying benign and malignant samples.

In the case of the classification model for early and late, maximal classifying performance may be obtained using 9 principal components. Appendix C9 shows the resulting SVM model. Such an SVM model may be used with a 340×9 transformation matrix comprising the first 9 columns of a matrix shown in Appendix B3 for classifying early and late samples.

Example 14 Diagnostic Test for Ovarian Tumours: Key Lipids

A major advantage of the targeted approach described here is its inherent foundation on a set of characterized lipid species. This is in complete contrast to previous proteomic studies which were based on pattern analysis of ions from largely unidentified and uncharacterized peptides.

Detailed comparison, using the Kruskal Wallis test (a non-parametric equivalence of ANOVA, FIG. 1), revealed a subset of ˜80 lipid (82) species which are sufficient to describe >99.9% of the variance between the different sets of cases and controls.

A further striking result which provides new information on potential association with ovarian cancer is the observation of mis-regulated choline lipids.

Based on the principal components and also by individual calculations of the differences, lipids responsible for the separation of controls from patients are identified.

Some of the few representative lipids are shown in FIG. 10. Contribution of the major lipid players in the diagnosis is depicted as a pie chart in FIG. 11. The lipids after removal of the standards and the backgrounds are narrowed down to 360 and is covered as a pie chart.

Percentages indicate the relative distribution among the 11 classes of lipids. The outer boundary of the chart depicts lyso (open) and non-lyso forms of lipids (filled). Only lipids with a difference and p<0.001 are shown. A blue circle indicates and increase in a particular lipid in malignant over benign forms of tumor while a red diamond reflects a decrease in the respective lipid in patient over control. Note the dramatic alterations of choline lipids (GPCho and SM) in plasma from patients with ovarian cancer. Some of the lipid species are represented as chemical structures. A list of the altered lipids between the cases and the controls is shown in Table E6.

TABLE E6 Lipids Responsible for Classification of Ovarian Cancers Lipid m/z Benign Malignant p-value  1 GPA: 36:0 703.8 29.71 80.76 <1.0E−06  2 GPA: 16:0/22:5 721.8 65.75 187.84 <1.0E−06  3 GPA: 38:0 731.8 44.66 109.80 <1.0E−06  4 GPGro:Lyso 16:0 483.4 89.99 175.45 <1.0E−06  5 GPGro:Lyso 18:0 511.4 89.22 186.02 <1.0E−06  6 GPGro: 18:2/18:2 769.8 101.89 68.33 <1.0E−06  7 GPGro: 18:0/18:0 777.8 57.67 175.25 <1.0E−06  8 GPEin:Lyso 18:2a 476.6 55.96 46.37 <1.0E−06  9 GPEtn:Lyso 18:1 478.4 61.14 53.12 <1.0E−06 10 GPEtn:Lyso 20:4 500.4 77.46 40.49 <1.0E−06 11 GPEtn:Lyso 22:6 524.4 96.20 51.64 <1.0E−06 12 GPCho: 32:0a 734.6 106.21 125.10 <1.0E−06 13 GPCho: 34:2e 744.6 81.22 65.69 <1.0E−06 14 GPCho: 36:2a 786.6 90.83 81.70 <1.0E−06 15 GPCho: 38:2a 814.6 103.44 127.24 <1.0E−06 16 Cer: d18:1/18:0 566.7 118.28 232.24 <1.0E−06 17 Cer: d18:1/20:0 594.7 96.63 172.62 <1.0E−06 18 Cer: d18:1/22:0 622.8 74.18 122.63 <1.0E−06 19 Cer: d18:1/24:1 648.9 84.79 167.95 <1.0E−06 20 SM: d18:1/18:0 731.8 120.26 154.36 <1.0E−06 21 SM: d18:1/22:0 787.9 89.88 88.01 <1.0E−06 22 SM: d18:1/24:1 813.9 106.69 132.41 <1.0E−06 23 GPA: 36:1 701.8 15.82 47.90   1.0E−06 24 GPCho: 36:3a 784.6 93.47 79.88   1.0E−06 25 GPCho: 40:5p, 40:6e 820.6 126.80 91.35   1.0E−06 26 GPCho: 40:6a 834.6 137.30 94.11   1.0E−06 27 SM: d18:1/26:0 843.9 135.42 92.25   1.0E−06 28 GPCho:Lyso 18:2 520.4 78.55 75.98   2.0E−06 29 GPCho: 38:5a 808.6 117.55 86.87   3.0E−06 30 GPA: 18:1/16:0 673.8 34.06 71.35   4.0E−06 31 Cer: d18:1/24:0 650.9 58.15 91.39    513E−06  32 GPCho: 38:5p, 38:6e 792.6 113.63 89.05   6.0E−06 33 DiHexCer: d18:1/18:0 890.7 93.33 143.80   7.0E−06 34 GPGro: 18:2/18:1 771.8 75.18 77.63   1.0E−05 35 GPCho: 36:2p, 36:3e 770.6 93.96 80.83   1.0E−05 36 GPA: 36:2 699.8 41.77 76.41   1.1E−05 37 GPCho: 28:0a 678.5 62.22 69.74   1.2E−05 38 MonoHexCer: 728.7 78.23 130.92   1.3E−05 d18:1/18:0 39 MonoHexCer: 810.9 96.74 147.14   1.9E−05 d18:1/ 24:1 40 Cer: d18:1/16:0 538.7 109.71 152.16   2.6E−05

Interestingly some of the GPEtn are also shown to be altered. The ovarian cancers have been shown to have an altered phosphatidylcholine metabolism. These three lipids are tightly interlinked both in their biochemical, synthetic and regulatory pathways.

There are reports of increased levels of choline phospholipids in the cancer cells. This is due to the increased activities of choline kinase, phospholipase C and phospholipase D in these cancer tissues. The exact role of these enzymes in carcinogenesis is not studied exclusively and remains inconclusive till date.

The overall reduction of choline lipids is mirrored by an increase in ceramides (Cer) and glycosylated ceramides (HexCer) pointing to activation of hydrolases with are specific for choline headgroups such as phospholipase (PLD) and sphingomyelinases (SMases). Thus it could be predictive that the levels of the lipids in the blood of the individuals could be a representative profile of these lipids in comparison to that in the ovarian tissues.

There could be a leakage or intake of these lipids through the ovarian cancer cells to or from blood, thereby altering their levels. Or else it could be a secondary effect upon the vascular system coming in contact with these abnormally growing cells. Further investigation needs to be carried out to answer the origin of this choline containing species.

Example 15 Diagnostic Test for Ovarian Tumours: Analysis Using Lipid Subset

Example 14 demonstrates that a subset of ˜80 lipid (82) species is sufficient to describe >99.9% of the variance between the different sets of cases and controls.

These 82 lipids are set out in Table D2 above.

Corresponding classification models may be formed by applying PCA and SVM on a training dataset comprising concentrations of the 82 lipids shown in Table D2, using the methods described above.

The classification models are described in detail below. They may be used to classify normal vs diseased, benign vs malignant and early vs late, etc as described above. Accordingly, classification can be conducted by simply determining the concentrations of these 82 lipids in a biological sample from an individual and applying the classification methods described in this document.

Appendix B4 shows an 82×82 transformation matrix derived from PCA analysis of a dataset comprising lipid concentrations of the 82 lipids shown in Table D2 in normal and diseased samples. Appendix C10 shows a corresponding SVM model. The transformation matrix of Appendix B4 and the SVM model of Appendix C10 may be used as a classification model for classifying normal and diseased samples.

Appendix B5 shows an 82×82 transformation matrix derived from PCA analysis of a dataset comprising lipid concentrations of the 82 lipids shown in Table D2 in benign and malignant samples. Appendix C11 shows a corresponding SVM model. The transformation matrix of Appendix B5 and the SVM model of Appendix C11 may be used as a classification model for classifying benign and malignant samples.

Appendix B6 shows an 82×82 transformation matrix derived from PCA analysis of a dataset comprising lipid concentrations of the 82 lipids shown in Table D2 in early and late samples. Appendix C12 shows a corresponding SVM model. The transformation matrix of Appendix B6 and the SVM model of Appendix C12 may be used as a classification model for classifying benign and malignant samples.

Example 16 Diagnostic Test for Ovarian Tumours: Analysis Using Choline Lipids

Example 14 demonstrates that levels of choline lipids are associated with ovarian cancer.

Table D1 is a list of 77 choline lipid species, comprising phosphatidylcholines (GPCho) and sphingomyelins (SM).

Corresponding classification models may be formed by applying PCA and SVM on a training dataset comprising concentrations the 77 choline lipids shown in Table D1.

FIG. 12 shows a receiver operating characteristics (ROC) curve. The ROC curve compares the performance of a classification model derived from analysis of the 77 choline lipids (GPCho and SM) in Table D1 (dashed line) and a classification model derived from analysis of the 340 lipids in Table D3 (solid line).

The classification models are described in detail below. They may be used to classify normal vs diseased, benign vs malignant and early vs late, etc as described above. Accordingly, classification can be conducted by simply determining the concentrations of these 77 choline lipids in a biological sample from an individual and applying the classification methods described in this document.

Appendix B7 shows an 77×77 transformation matrix derived from PCA analysis of a dataset comprising lipid concentrations of the 77 choline lipids shown in Table D1 in normal and diseased samples. Appendix C13 shows a corresponding SVM model. The transformation matrix of Appendix B7 and the SVM model of Appendix C13 may be used as a classification model for classifying normal and diseased samples.

Appendix B8 shows an 77×77 transformation matrix derived from PCA analysis of a dataset comprising lipid concentrations of the 77 choline lipids shown in Table D1 in benign and malignant samples. Appendix C14 shows a corresponding SVM model. The transformation matrix of Appendix B8 and the SVM model of Appendix C14 may be used as a classification model for classifying benign and malignant samples.

Appendix B9 shows an 77×77 transformation matrix derived from PCA analysis of a dataset comprising lipid concentrations of the 77 choline lipids shown in Table D1 in early and late samples. Appendix C15 shows a corresponding SVM model. The transformation matrix of Appendix B9 and the SVM model of Appendix C15 may be used as a classification model for classifying benign and malignant samples.

Example 17 Diagnostic Test for Ovarian Tumours: Blinded Tests

To confirm our findings, we subsequently challenged our models to another set of independent 73 test samples which had been blinded before the prediction.

The analysis is done in a pseudo clinical setup in collaboration with the affiliated hospital. The samples are obtained in a set of 5 and the lipid extracts are carried out immediately after obtaining the samples. The lipids are analyzed on the mass spectrometry in a set of 10. This is to introduce day-to-day disparity in the extraction methods and also the mass spectrometry settings.

The prediction process is depicted in the flowchart shown in FIG. 13.

The demographic profiles of these 73 samples are depicted in Table E7.

TABLE E7 Demographic Profiling of Plasma Samples in Blinded Set BLIND SAMPLES Ave Age Max Min Chinese Malay Indian Other/NA Sum Control NA NA NA 3 3 3 5 14 Patient 48 81 16 27 21 5 6 59 Benign 46 81 16 20 18 3 6 47 Malignant 49 74 16 7 3 2 0 12 Early 53 71 23 1 1 0 0 2 Late 47 74 16 6 2 2 0 10 73 BLIND SAMPLES Benign Malignant Early Late Endometroid 11 2 1 1 Mucinous 8 5 1 4 Ser/Pap 8 3 0 3 Dermoid 11 0 0 0 Physiol/foll 1 0 0 0 other 8 2 0 2 Clear Cell 0 0 0 0 Mixed Germ 0 0 0 0 47 12 2 10

The blinded samples are passed through the SVM models which separated the samples into control, benign or malignant (FIG. 14).

In this scenario, a blinded sample passes through a maximum of 3 SVM models for full prediction. Each step makes a binary decision.

The first model (also known as “SVM-1”) predicts whether the sample shows ‘patient’ or ‘control’ characteristic.

If classified as ‘patient’ it will continue through a second model (also known as “SVM-2”) which predicts whether it is ‘benign’ or ‘malignant’.

The third SVM (also known as “SVM-3”) discriminates early vs. late stages.

Each of the blinded samples is passed through the predictors.

The predictions for all the 73 test samples are obtained and then the samples are un-blinded.

Example 18 Diagnostic Test for Ovarian Tumours: Blinded Tests Results

The lipidomic based biomarker is able to correctly diagnose 8 of the 11 malignant patients, 45 out of 48 benign patients and 10 out of 14 samples as normals. So in all 63 out of the 73 samples are predicted accurately. This rate of prediction seems to be quite reasonable.

The SVM also predicted the late from the early malignant samples but the number of malignant samples is observed to be very low and hence is not taken for statistical considerations. This is unavoidable as these sets of blinded samples are randomly separated from the original set of 211.

The prediction summary is depicted in Table E8.

TABLE E8 Prediction Summary for the Blinded Samples Based on the SVM Models Control Benign Early Cont + Benign Vs Vs Vs Vs Patient Malignant Late Malignant True Positive (TP) 55 8 1 8 True Negative (TN) 10 45 3 58 False Positive (FP) 4 3 5 4 False Negative (FN) 4 3 0 3 Total 73 59 9 73 Sensitivity 93.22 72.73 100.00 72.73 Specificity 71.43 93.75 37.50 93.55 PPV 93.22 72.73 16.67 66.67 NPV 71.43 93.75 100.00 95.08 Accuracy 89.04 89.83 44.44 90.41

The model is able to differentiate between the controls and the patients with a PPV and NPV of 93% (55 of 59) and 71% (10 of 14) with 89% accuracy (65 of 73), 93% sensitivity (55 of 59) and 71% specificity (10 of 14).

The predicted patients samples (59) are passed through the second predictor (SVM-2) to distinguish the benign from the malignant, the PPV and NPV obtained is 72% and 93% respectively with an accuracy of 89% (sensitivity 73%, specificity 94%).

The blinded samples are observed to have less number of malignant samples (11) and hence with this low number the third model (SVM-3) even though it is supposed to be a good predictor would not give a statistically significant data and hence is not used for prediction. Another model for the separation of the malignant from the controls and benign is able to give a PPV and NPV of 67% and 95% respectively with an accuracy of 90%.

Even though upon comparison between the model and the blinded samples, the blinded samples did not perform to the expectations, they still seem to justify the use of lipids as biomarker tools. This is so because the markers are able to differentiate between the benign against malignant cancer harboring patients which itself seems quite novel.

Example 19 Discussion

The goal of our study is not only to detect the patients from the normal but also to separate the benign from the malignant. This is because based on the present clinical scenario there are no available tools to identify the benign from the malignant unless the growth is surgically removed and goes to various pathological testing. We are able to do so and also extrapolate our model to separate the late from the early stages of malignancy. Our model worked very well in comparison to the CA125 test. Even though the CA125 showed slightly better performance than the previously reported work, it still could not match the performance of the lipid based biomarkers.

The strength of this method relies not only on using one set or class of biomarkers but a complete profile and then use it as a diagnostic tool. This reduces the room for error as the lipids which are screened as very high. Also the level of the sample that is needs for this type of analysis is very low.

As low as 50 μl of plasma sample which could be easily obtained from the patient is able to diagnose not only whether there is a formation of cyst but also whether this growth is benign or malignant. This will reduce the burden of the patients from going into unwanted surgical procedures and trauma. It could be emphasized that the CA125 in conjunction with the lipid based biomarker and the TVU could also increase then have a very good predictive rate.

This methodology of using MRM for the analysis is novel and there have been no reports to our knowledge to date which uses this robust way of quantification of lipids. This methodology seems superior to the semi-quantitative analysis using the lipid spectra as the spectra could be a mix of more than one species. And also one would need to do further studies to confirm the presence of the regulated species. This makes it less clinically significant.

In conclusion, the lipidomic based biomarker profiling can not only be used for the ovarian cancer but can be extrapolated to other pathological conditions.

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Each of the applications and patents mentioned in this document, and each document cited or referenced in each of the above applications and patents, including during the prosecution of each of the applications and patents (“application cited documents”) and any manufacturer's instructions or catalogues for any products cited or mentioned in each of the applications and patents and in any of the application cited documents, are hereby incorporated herein by reference. Furthermore, all documents cited in this text, and all documents cited or referenced in documents cited in this text, and any manufacturer's instructions or catalogues for any products cited or mentioned in this text, are hereby incorporated herein by reference.

Various modifications and variations of the described methods and system of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in molecular biology or related fields are intended to be within the scope of the claims.

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LENGTHY TABLES The patent application contains a lengthy table section. A copy of the table is available in electronic form from the USPTO web site (http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20110021451A1). An electronic copy of the table will also be available from the USPTO upon request and payment of the fee set forth in 37 CFR 1.19(b)(3). 

1-39. (canceled)
 40. A method of determining a status of a sample, the method comprising (I) providing a dataset comprising concentrations of a plurality of lipids in the sample, and (II) applying a classification model according to one of (A)-(C) as follows to generate a transformed dataset: Classification Model A: (i) a 340×340 transformation matrix as shown in Appendix B1 and an SVM model as shown in Appendix C1; (ii) a 340×85 transformation matrix comprising the first 85 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C4; (iii) a 340×10 transformation matrix comprising the first 10 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C7; (iv) a 82×82 transformation matrix as shown in Appendix B4 and an SVM model as shown in Appendix C10; or (v) a 77×77 transformation matrix as shown in Appendix B7 and an SVM model as shown in Appendix C13; Classification Model B: (i) a 340×340 transformation matrix as shown in Appendix B2 and an SVM model as shown in Appendix C2; (ii) a 340×87 transformation matrix comprising the first 87 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C5; (iii) a 340×29 transformation matrix comprising the first 29 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C8; (iv) a 82×82 transformation matrix as shown in Appendix B5 and an SVM model as shown in Appendix C11; or (v) a 77×77 transformation matrix as shown in Appendix B8 and an SVM model as shown in Appendix C14; or Classification Model C: (i) a 340×340 transformation matrix as shown in Appendix B3 and an SVM model as shown in Appendix C3; (ii) a 340×44 transformation matrix comprising the first 44 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C6; (iii) a 340×9 transformation matrix comprising the first 9 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C9; (iv) a 82×82 transformation matrix as shown in Appendix B6 and an SVM model as shown in Appendix C12; or (v) a 77×77 transformation matrix as shown in Appendix B9 and an SVM model as shown in Appendix C15; and (III) subjecting the transformed dataset of step (II) to SVM analysis with an SVM model of the classification model, in which: a) when a classification model according to Classification Model A is used, an output of >0 indicates a normal sample, and an output of <0 indicates an ovarian cancer sample; b) when a classification model according to Classification Model B is used, an output of >0 indicates a benign sample, and an output of <0 indicates a malignant sample; and c) when a classification model according to Classification Model C is used, an output of >0 indicates an early stage ovarian cancer sample, and an output of <0 indicates a late stage ovarian cancer sample; whereby a status of the sample is determined.
 41. The method of claim 40, which is a computer implemented method.
 42. The method of claim 40 further comprising the step, before step (I), of measuring the concentration of a plurality of lipids in a sample from or of the individual to generate a dataset comprising concentrations of said lipids.
 43. The method of claim 40, in which the plurality of lipids includes a plurality of choline lipids, and further optionally comprises phosphatidic acid (GPA), phosphatidylglycerol (GPGro), phosphatidylserine acid (GPSer), sulfatides, cardiolipin, phosphatidylethanolamine (GPEtn), phosphatidylinositol (GPIns), phosphatidylinositol phosphates (GPInsPs), ceramide (Cer), mono hexosyl ceramide (MonoHexCer) and di hexosyl ceramide (DiHexCer).
 44. The method of claim 43 wherein the choline lipids include phosphatidylcholine (GPCho) or sphingomyelin (SM) or both.
 45. The method of claim 40 wherein the plurality of lipids comprises the lipids set out in Table D1, Table D2, Table D3, Table E4 or Table E6.
 46. The method of claim 40 in which the sample comprises a serum sample of or from an individual.
 47. The method of claim 42 wherein the lipids are identified by mass spectroscopy, electrospray ionization mass spectrometry (ESI-MS), or quantified by multiple reaction monitoring (MRM), or both ESI-MS and MRM.
 48. The method of claim 42 claim, in which the concentration of each lipid is normalized by obtaining ${{Lipid}_{i} = \frac{x_{i}}{\lbrack{Std}\rbrack \cdot {\sum\limits_{i = 1}^{n}x_{i}}}},$ where x_(i) is the intensity of a lipid, and Std is the ratio of the intensity to the amount in pmoles of a lipid standard.
 49. A classification model obtained by the method of claim
 40. 50. A method for detecting, and optionally classing, ovarian cancer in an individual, the method comprising: (a) measuring the concentration of a plurality of lipids set out in Table D1, Table D2, Table D3, Table E4 or Table E6 in a sample from or of the individual to generate a dataset comprising concentrations of said lipids; (b) transforming the dataset of step (a) with a transformation matrix of a classification model comprising: (i) a 340×340 transformation matrix as shown in Appendix B1 and a Support Vector Machines (SVM)) model as shown in Appendix C1; (ii) a 340×85 transformation matrix comprising the first 85 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C4; (iii) a 340×10 transformation matrix comprising the first 10 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C7; (iv) a 82×82 transformation matrix as shown in Appendix B4 and an SVM model as shown in Appendix C10; or (v) a 77×77 transformation matrix as shown in Appendix B7 and an SVM model as shown in Appendix C13; to generate a transformed dataset, and subjecting the transformed dataset to SVM analysis with an SVM model of the classification model, in which an output of >0 indicates a normal sample and an output of <0 indicates an ovarian cancer sample; and (c) in the case of the latter, further transforming the dataset with a transformation matrix of a classification model comprising: (i) a 340×340 transformation matrix as shown in Appendix B2 and an SVM model as shown in Appendix C2; (ii) a 340×87 transformation matrix comprising the first 87 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C5; (iii) a 340×29 transformation matrix comprising the first 29 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C8; (iv) a 82×82 transformation matrix as shown in Appendix B5 and an SVM model as shown in Appendix C11; or (v) a 77×77 transformation matrix as shown in Appendix B8 and an SVM model as shown in Appendix C14; to generate a transformed dataset and subjecting the transformed dataset to SVM analysis with an SVM model of the classification model; in which an output of <0 indicates a benign sample and an output of <0 indicates a malignant sample; and (d) in the case of the latter, further transforming the dataset with a transformation matrix of a classification model comprising: (i) a 340×340 transformation matrix as shown in Appendix B3 and an SVM model as shown in Appendix C3; (ii) a 340×44 transformation matrix comprising the first 44 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C6; (iii) a 340×9 transformation matrix comprising the first 9 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C9; (iv) a 82×82 transformation matrix as shown in Appendix B6 and an SVM model as shown in Appendix C12; or (v) a 77×77 transformation matrix as shown in Appendix B9 and an SVM model as shown in Appendix C15; to generate a transformed dataset and subjecting the transformed dataset to SVM analysis with an SVM model of the classification model; in which an output of >0 indicates an early stage ovarian cancer sample and an output of <0 indicates a late stage ovarian cancer sample.
 51. A classification model capable of distinguishing between a normal sample and an ovarian cancer sample, the classification model comprising: (a) a 340×340 transformation matrix as shown in Appendix B1 and an SVM model as shown in Appendix C1; (b) a 340×85 transformation matrix comprising the first 85 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C4; (c) a 340×10 transformation matrix comprising the first 10 columns of the matrix as shown in Appendix B1 and an SVM model as shown in Appendix C7; (d) a 82×82 transformation matrix as shown in Appendix B4 and an SVM model as shown in Appendix C10; or (e) a 77×77 transformation matrix as shown in Appendix B7 and an SVM model as shown in Appendix C13.
 52. A computer readable medium comprising a classification model according to claim
 51. 53. A classification model capable of distinguishing between a benign cancer sample and an malignant cancer sample, the classification model comprising: (a) a 340×340 transformation matrix as shown in Appendix B2 and an SVM model as shown in Appendix C2; (b) a 340×87 transformation matrix comprising the first 87 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C5; (c) a 340×29 transformation matrix comprising the first 29 columns of the matrix as shown in Appendix B2 and an SVM model as shown in Appendix C8; (d) a 82×82 transformation matrix as shown in Appendix B5 and an SVM model as shown in Appendix C11; or (e) a 77×77 transformation matrix as shown in Appendix B8 and an SVM model as shown in Appendix C14.
 54. A computer readable medium comprising a classification model according to claim
 53. 55. A classification model capable of distinguishing between an early stage cancer sample and a late stage cancer sample, the classification model comprising: (a) a 340×340 transformation matrix as shown in Appendix B3 and an SVM model as shown in Appendix C3; (b) a 340×44 transformation matrix comprising the first 44 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C6; (c) a 340×9 transformation matrix comprising the first 9 columns of the matrix as shown in Appendix B3 and an SVM model as shown in Appendix C9; (d) a 82×82 transformation matrix as shown in Appendix B6 and an SVM model as shown in Appendix C12; or (e) a 77×77 transformation matrix as shown in Appendix B9 and an SVM model as shown in Appendix C15.
 56. A computer readable medium comprising a classification model according to claim
 55. 57. A method of generating a classification model capable of distinguishing between two biological states, the method comprising the steps of: (a) providing a training dataset, X, comprising concentrations of a plurality of lipids in a biological sample in a first state and a biological sample in a second state; (b) subjecting the training dataset X to Principal Components Analysis (PCA), in which the PCA analysis generates a transformation matrix, C, and a transformed dataset, Y_(l); (c) subjecting the transformed dataset Y_(l) to Support Vector Machines (SVM) analysis, in which the SVM analysis generates a SVM model, S; (d) forming a classification model comprising (i) the transformation matrix C, and (ii) the corresponding SVM model S.
 58. The method of claim 57 in which the two biological states are selected from a normal state and a diseased state, a benign state and a malignant state, and an early tumour stage and a late tumour stage.
 59. The method of claim 57 which further comprises a step (c1) between step (c) and step (d), comprising repeating steps (b) and (c) and selecting principal components which enable optimal classification in step (c).
 60. The method of claim 59, in which optimal classification in step (c1) is determined by assessing the output of the SVM for sensitivity, specificity and accuracy at each iteration.
 61. The method of claim 59, in which the classification model further comprises (iii) the number of selected principal components enabling optimal classification in step (c).
 62. The method of claim 59, in which step (c1) comprises one or more of the following: (a) retaining principal components that perform at least 55%, 65%, 75%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9% or 100% as well as the full dataset, after step (e), as assessed by any combination of sensitivity, specificity, PPV, NPV, accuracy, true negatives (TN), false negatives (FN), false positives (FP) and true positives (TP); (b) removing factors which do not significantly affect the performance of the SVM model; (c) retaining principal components whose eigenvalues are greater than or equal to 1; (d) retaining principal components that explain at least 55%, 65%, 75%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.6%, 99.7%, 99.8% or 99.9% of the variance in the dataset; and (e) retaining principal components that in a scree plot of eigenvalues show a smooth decrease of eigenvalues or which are to the left of a levelling off or significant decrease in gradient or elbow in the plot (scree test).
 63. A method of treatment or prevention of ovarian cancer in an individual, the method comprising detecting or diagnosing the cancer and optionally classing the cancer, in an individual by a method of claim 50, and administering a suitable treatment or prophylactic to the individual.
 64. A method of generating a classification model capable of distinguishing between two biological states, the method comprising the steps of: (a) providing a training dataset, X, comprising concentrations of a plurality of lipids in a biological sample in a first state and a biological sample in a second state; (b) subjecting the training dataset X to Principal Components Analysis (PCA) to generate a transformation matrix, C, comprising principal component coefficients; and a representation, Y, of the dataset X in the principal component space; (c) forming an input vector, Y_(l), comprising the l most significant row vectors of Y; (d) subjecting the input vector Y_(l) to Support Vector Machines (SVM) analysis; (e) repeating steps (c) and (d) with varying/to determine a minimum dimension, l_(min), of the principal component space sufficient to obtain optimal classification; (f) forming a classification model comprising (i) the transformation matrix C, (ii) the minimum dimension l_(/min), and (ii) the SVM model comprising SVM weights corresponding to the minimum dimension l_(min).
 65. A classification model on a computer-readable medium, the model selected from the group consisting of: (a) a classification model capable of distinguishing between a normal sample and a diseased sample comprising: (i) an n×m transformation matrix, where n<340 and 1≦m≦n, comprising the first m columns of the matrix shown in Appendix B1; and (ii) an SVM model generated from applying SVM analysis on a transformed dataset, the transformed dataset being generated by transforming a dataset comprising concentrations of the first nlipids shown in Table D3 in a normal sample and a diseased sample with an n×m transformation of (a)(i); (b) a classification model capable of distinguishing between a benign sample and a malignant sample comprising: (i) an n×m transformation matrix, where n<340 and 1≦m≦n, comprising the first m columns of the matrix shown in Appendix B2; and (ii) an SVM model generated from applying SVM analysis on a transformed dataset, the transformed dataset being generated by transforming a dataset comprising concentrations of the first nlipids shown in Table D3 in a benign sample and a malignant sample with an n×m transformation matrix of (b)(i); (c) a classification model capable of distinguishing between an early stage sample and a late stage sample comprising: (i) an n×m transformation matrix, where n<340 and 1≦m≦n, comprising the first m columns of the matrix shown in Appendix B3; and (ii) an SVM model generated from applying SVM analysis on a transformed dataset, the transformed dataset being generated by transforming a dataset comprising concentrations of the first nlipids shown in Table D3 in an early stage sample and a late stage sample with an n×m transformation matrix of (c)(i). 